Publications

Publications of the Statistical machine learning and bioinformatics group.

to appear

• Ali Faisal, Jussi Gillberg, Jaakko Peltonen, Gayle Leen, and Samuel Kaski. Sparse nonparametric topic model for transfer learning. In Proceedings of 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, to appear. PDF (127 kB) [More info]
• Suleiman A. Khan, Ali Faisal, John Patrick Mpindi, Juuso A. Parkkinen, Tuomo Kalliokoski, Antti Poso, Olli P. Kallioniemi, Krister Wennerberg, and Samuel Kaski. Comprehensive data-driven analysis of the impact of chemoinformatic structure on the genome-wide biological response profiles of cancer cells to 1159 drugs. BMC Bioinformatics, to appear. [More info]

  2012

• José Caldas, Nils Gehlenborg, Eeva Kettunen, Ali Faisal, Mikko Rönty, Andrew G. Nicholson, Sakari Knuutila, Alvis Brazma, and Samuel Kaski. Data-driven information retrieval in heterogeneous collections of transcriptomics data links SIM2s to malignant pleural mesothelioma. Bioinformatics, 28:246–253, 2012. Supplementary data and source code are available from http://www.ebi.ac.uk/fg/research/rex. PDF (316 kB) [More info]
• Jukka Corander, Tero Aittokallio, Samuli Ripatti, and Samuel Kaski. The rocky road to personalized medicine: computational and statistical challenges. Personalized Medicine, 9:109–114, 2012. PDF (575 kB) [More info]
• Mehmet Gönen. Bayesian efficient multiple kernel learning. In Proceedings of the 29th International Conference on Machine Learning (ICML), 2012. [More info]
• Mehmet Gönen. Bayesian supervised multilabel learning with coupled embedding and classification. In Proceedings of 2012 SIAM International Conference on Data Mining (SDM), 2012. [More info]
• Miika Koskinen, Jaakko Viinikanoja, Mikko Kurimo, Arto Klami, Samuel Kaski, and Riitta Hari. Identifying fragments of natural speech from the listener's MEG signals. Human Brain Mapping, 2012. [More info] [See also: dx.doi.org ...]
• Jaakko Peltonen, Tapani Raiko, and Samuel Kaski, editors. Neurocomputing, Special Issue on Machine Learning for Signal Processing 2010, 80:1-128, 2012. [More info] [See also: dx.doi.org ...]
• Seppo Virtanen, Arto Klami, Suleiman A. Khan, and Samuel Kaski. Bayesian group factor analysis. In Neil Lawrence and Mark Girolami, editors, Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, volume 22 of JMLR W&CP, pages 1269–1277. JMLR, 2012. PDF (444 kB) [More info] [See also: jmlr.csail.mit.edu ...]

  2011

• Antti Ajanki and Samuel Kaski. Probabilistic proactive timeline browser. In Timo Honkela, Wlodzislaw Duch, Mark A. Girolami, and Samuel Kaski, editors, Proceedings of the 21st International Conference on Artificial Neural Networks (ICANN), Part II, Lecture Notes in Computer Science, pages 357–364, Berlin, 2011. Springer. PDF (1 MB) [More info] [See also: dx.doi.org ...]
• Antti Ajanki, Mark Billinghurst, Hannes Gamper, Toni Järvenpää, Melih Kandemir, Samuel Kaski, Markus Koskela, Mikko Kurimo, Jorma Laaksonen, Kai Puolamäki, Teemu Ruokolainen, and Timo Tossavainen. An augmented reality interface to contextual information. Virtual Reality, 15(2-3):161–173, 2011. PDF (3 MB) [More info] [See also: dx.doi.org ...]
• Ioana Borze, Mohamed Guled, Suad Musse, Anna Raunio, Erkki Elonen, Ulla Saarinen-Pihkala, Marja-Liisa Karjalainen-Lindsberg, Leo Lahti, and Sakari Knuutila. MicroRNA microarrays on archive bone marrow core biopsies of leukemias - method validation. Leukemia Research, 35(2):188–195, 2011. [More info] [See also: www.sciencedirect.com ...]
• José Caldas and Samuel Kaski. Hierarchical generative biclustering for microRNA expression analysis. Journal of Computational Biology, 18:251–261, 2011. [More info] [See also: www.liebertonline.com ...]
• Ali Faisal, Riku Louhimo, Leo Lahti, Sampsa Hautaniemi, and Samuel Kaski. Biomarker discovery via dependency analysis of multi-view functional genomics data. In NIPS 2011 workshop "From Statistical Genetics to Predictive Models in Personalized Medicine", 2011. Extended abstract. PDF (393 kB) [More info]
• Riku Louhimo, Viljami Aittomäki, Ali Faisal, Marko Laakso, Ping Chen, Kristian Ovaska, Erkka Valo, Leo Lahti, Vladimir Rogojin, Samuel Kaski, and Sampsa Hautaniemi. Systematic use of computational methods allows stratifying treatment responders in glioblastoma multiforme. In Proceedings of CAMDA 2011 conference, Critical Assessment of Massive Data Analysis, 2011. Source code available at http://csbi.ltdk.helsinki.fi/camda/. PDF (505 kB) [More info]
• Mehmet Gönen, Melih Kandemir, and Samuel Kaski. Multitask learning using regularized multiple kernel learning. In Bao-Liang Lu, Liqing Zhang, and James Kwok, editors, Proceedings of 18th International Conference on Neural Information Processing (ICONIP), volume 7063 of Lecture Notes in Computer Science, pages 500–509, Berlin / Heidelberg, 2011. Springer. PDF (212 kB) [More info]
• Mehmet Gönen, Aydin Ulas, Peter Schüffler, Umberto Castellani, and Vittorio Murino. Combining data sources nonlinearly for cell nucleus classification of renal cell carcinoma. In Marcello Pelillo and Edwin Hancock, editors, Proceedings of 1st International Workshop on Similarity-Based Pattern Analysis and Recognition (SIMBAD), volume 7005 of Lecture Notes in Computer Science, pages 250–260, Berlin / Heidelberg, 2011. Springer. PDF (462 kB) [More info]
• Timo Honkela, Wlodzislaw Duch, Mark Girolami, and Samuel Kaski, editors. Artificial Neural Networks and Machine Learning Research - ICANN 2011, Part I, Berlin, 2011. Springer. [More info] [See also: www.springerlink.com ...]
• Timo Honkela, Wlodzislaw Duch, Mark Girolami, and Samuel Kaski, editors. Artificial Neural Networks and Machine Learning Research - ICANN 2011, Part II, Berlin, 2011. Springer. [More info] [See also: www.springerlink.com ...]
• Samuel Kaski and Jaakko Peltonen. Dimensionality reduction for data visualization. IEEE Signal Processing Magazine, 28(2):100–104, 2011. [More info] [See also: dx.doi.org ...]
• Arto Klami, editor. Proceedings of ICANN/PASCAL2 Challenge: MEG Mind Reading, Aalto University Publication series SCIENCE + TECHNOLOGY 29/2011, Espoo, 2011. PDF (220 kB) [More info]
• Arto Klami, Pavan Ramkumar, Seppo Virtanen, Lauri Parkkonen, Riitta Hari, and Samuel Kaski. ICANN/PASCAL2 challenge: MEG mind reading – overview and results. In Arto Klami, editor, Proceedings of ICANN/PASCAL2 Challenge: MEG Mind Reading, Aalto University Publication series SCIENCE + TECHNOLOGY 29/2011, pages 3–19, Espoo, 2011. PDF (117 kB) [More info]
• Leo Lahti, Laura L. Elo, Tero Aittokallio, and Samuel Kaski. Probabilistic analysis of probe reliability in differential gene expression studies with short oligonucleotide arrays. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 8:217–225, 2011. PDF (162 kB) [More info] [See also: www.computer.org ...]
• Gayle Leen, Jaakko Peltonen, and Samuel Kaski. Focused multi-task learning using Gaussian processes. In Dimitrios Gunopulos, Thomas Hofmann, Donato Malerba, and Michalis Vazirgiannis, editors, Machine Learning and Knowledge Discovery in Databases (Proceedings of ECML PKDD 2011), Part II, pages 310–325. Springer Berlin / Heidelberg, 2011. Best Paper Award in Machine Learning. PDF (721 kB) [More info] [See also: dx.doi.org ...]
• Tarja Niini, Leo Lahti, Francesca Michelacci, Shinsuke Ninomiya, Claudia Maria Hattinger, Mohamed Guled, Tom Böhling, Piero Picci, Massimo Serra, and Sakari Knuutila. Array comparative genomic hybridization reveals frequent alterations of G1/S checkpoint genes in undifferentiated pleomorphic sarcoma of bone. Genes, Chromosomes and Cancer, 50(5):291–306, 2011. [More info] [See also: dx.doi.org ...]
• Penny Nymark, Mohamed Guled, Ioana Borze, Ali Faisal, Leo Lahti, Kaisa Salmenkivi, Eeva Kettunen, Sisko Anttila, and Sakari Knuutila. Integrative analysis of microRNA, mRNA and aCGH data reveals asbestos- and histology-related changes in lung cancer. Genes, Chromosomes and Cancer, 50(8):585–597, 2011. [More info] [See also: www.ncbi.nlm.nih.gov ...]
• Jaakko Peltonen and Samuel Kaski. Generative modeling for maximizing precision and recall in information visualization. In Geoffrey Gordon, David Dunson, and Miroslav Dudik, editors, Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, volume 15 of JMLR W&CP, pages 597–587. JMLR, 2011. PDF (2 MB) [More info] [See also: jmlr.csail.mit.edu ...]
• Suvi Savola, Arto Klami, Samuel Myllykangas, Cristina Manara, Katia Scotlandi, Piero Ricci, Sakari Knuutila, and Jukka Vakkila. High expression of complement component 5 (C5) at tumor site associates with superior survival in Ewing's sarcoma family of tumour patients. ISRN Oncology, 2011, 2011. PDF (3 MB) [More info]
• Tommi Suvitaival, Ilkka Huopaniemi, Matej Orešic, and Samuel Kaski. Cross-species translation of multi-way biomarkers. In Timo Honkela, Wlodzislaw Duch, Mark Girolami, and Samuel Kaski, editors, Proceedings of the 21st International Conference on Artificial Neural Networks (ICANN), Part I, volume 6791 of Lecture Notes in Computer Science, pages 209–216. Springer, 2011. PDF (829 kB) [More info] [See also: dx.doi.org ...]
• Tommi Suvitaival, Ilkka Huopaniemi, Matej Orešic, and Samuel Kaski. Detecting similar high-dimensional responses to experimental factors between human and model organism. In NIPS 2011 workshop "From Statistical Genetics to Predictive Models in Personalized Medicine", 2011. Extended abstract. PDF (769 kB) [More info]
• Marko Sysi-Aho, Andrey Ermolov, Peddinti V. Gopalacharyulu, Abhishek Tripathi, Tuulikki Seppänen-Laakso, Johanna Maukonen, Ismo Mattila, Suvi T. Ruohonen, Laura Vähätalo, Laxman Yetukuri, Taina Härkönen, Erno Lindfors, Janne Nikkilä, Jorma Ilonen, Olli Simell, Maria Saarela, Mikael Knip, Samuel Kaski, Eriika Savontaus, and Matej Orešic. Metabolic regulation in progression to autoimmune diabetes. PLoS Computational Biology, 7:e1002257, 2011. PDF (2 MB) [More info] [See also: dx.doi.org ...]
• Seppo Virtanen, Arto Klami, and Samuel Kaski. Bayesian CCA via group sparsity. In Lise Getoor and Tobias Scheffer, editors, Proceedings of the 28th International Conference on Machine Learning (ICML-11), ICML '11, pages 457–464, New York, NY, 2011. ACM. Implementation in R available at http://research.ics.tkk.fi/mi/software/gsCCA/. PDF (258 kB) [More info]
• S.V.N. Vishwanathan, Samuel Kaski, Jennifer Neville, and Stefan Wrobel, editors. Machine Learning, Special Issue on Learning and Mining with Graphs, 82(2), 2011. [More info] [See also: www.springerlink.com ...]
• Abhishek Tripathi, Arto Klami, Matej Orešic, and Samuel Kaski. Matching samples of multiple views. Data Mining and Knowledge Discovery, 23:300–321, 2011. [More info] [See also: dx.doi.org ...]
• Laxman Yetukuri, Ilkka Huopaniemi, Artturi Koivuniemi, Marianna Maranghi, Anne Hiukka, Heli Nygren, Samuel Kaski, Marja-Riitta Taskinen, Ilpo Vattulainen, Matti Jauhiainen, and Matej Orešic. High density lipoprotein structural changes and drug response in lipidomic profiles following the long-term fenofibrate therapy in the FIELD substudy. PLoS ONE, 6(8):e23589, 2011. PDF (713 kB) [More info] [See also: dx.plos.org ...]

  2010

• Leo Lahti. Probabilistic analysis of the human transcriptome with side information. PhD thesis, Aalto University School of Science and Technology, Faculty of Information and Natural Sciences, Department of Information and Computer Science, Espoo, December 2010. The LaTeX sources and the pdf version are freely available under cc-by license at http://www.iki.fi/Leo.Lahti. PDF (4 MB) [More info] [See also: lib.tkk.fi ...]
• Leo Lahti, Antonio Gusmao, and Olli-Pekka Huovilainen. Netresponse (functional network analysis). BioConductor, October 2010. Computer program. [More info] [See also: www.bioconductor.org ...]
• Antti Ajanki, Mark Billinghurst, Hannes Gamper, Toni Järvenpää, Melih Kandemir, Samuel Kaski, Markus Koskela, Mikko Kurimo, Jorma Laaksonen, Kai Puolamäki, Teemu Ruokolainen, and Timo Tossavainen. Contextual information access with augmented reality. In Proceedings of MLSP 2010, IEEE International Workshop on Machine Learning for Signal Processing, pages 95–100. IEEE, August 2010. [More info] [See also: dx.doi.org ...]
• Leo Lahti and Olli-Pekka Huovilainen. Dependency modeling toolkit. ICML workshop, June 2010. Computer program. [More info] [See also: dmt.r-forge.r-project.org ...]
• Eerika Savia. Mutual Dependency-Based Modeling of Relevance in Co-Occurrence Data. PhD thesis, Aalto University School of Science and Technology, Faculty of Information and Natural Sciences, Department of Information and Computer Science, Espoo, June 2010. PDF (1 MB) [More info] [See also: lib.tkk.fi ...]
• Olli-Pekka Huovilainen and Leo Lahti. pint: Pairwise integration of functional genomics data. BioConductor, April 2010. Computer program. [More info] [See also: bioconductor.org ...]
• Antti Ajanki, Mark Billinghurst, Melih Kandemir, Samuel Kaski, Markus Koskela, Mikko Kurimo, Jorma Laaksonen, Kai Puolamäki, and Timo Tossavainen. Ubiquitous contextual information access with proactive retrieval and augmentation. In The Fourth International Workshop on Ubiquitous Virtual Reality (IWUVR2010), 2010. PDF (3 MB) [More info]
• Peter Auer, Zakria Hussain, Samuel Kaski, Arto Klami, Jussi Kujala, Jorma Laaksonen, Alex P. Leung, Kitsuchart Pasupa, and John Shawe-Taylor. Pinview: Implicit feedback in content-based image retrieval. In Tom Diethe, Nello Cristianini, and John Shawe-Taylor, editors, Proceedings of Workshop on Applications of Pattern Analysis, volume 11 of JMLR Workshop and Conference Proceedings, pages 51–57, 2010. [More info] [See also: jmlr.csail.mit.edu ...]
• José Caldas and Samuel Kaski. Hierarchical generative biclustering for microRNA expression analysis. In Bonnie Berger, editor, Research in Computational Molecular Biology, Proceedings of 14th Annual International Conference RECOMB 2010, Lisbon, Portugal, April 25-28, pages 65–79, Berlin, 2010. Springer. PDF (375 kB) [More info]
• Ali Faisal, Frank Dondelinger, Dirk Husmeier, and Colin M. Beale. Inferring species interaction networks from species abundance data: A comparative evaluation of various statistical and machine learning methods. Ecological Informatics, 5:451–464, 2010. [More info] [See also: dx.doi.org ...]
• Ilkka Huopaniemi, Tommi Suvitaival, Janne Nikkilä, Matej Orešic, and Samuel Kaski. Multivariate multi-way analysis of multi-source data. Bioinformatics, 26:i391–i398, 2010. (ISMB 2010). PDF (678 kB) [More info]
• Ilkka Huopaniemi, Tommi Suvitaival, Matej Orešic, and Samuel Kaski. Graphical multi-way models. In José Balcázar, Francesco Bonchi, Aristides Gionis, and Michèle Sebag, editors, Machine Learning and Knowledge Discovery in Databases. Proceedings of European Conference, ECML PKDD 2010, Barcelona, Spain, September 20-24, 2010, volume I, pages 538–553, Berlin, 2010. Springer. PDF (964 kB) [More info]
• Melih Kandemir, Veli-Matti Saarinen, and Samuel Kaski. Inferring object relevance from gaze in dynamic scenes. In Proceedings of ETRA 2010, ACM Symposium on Eye Tracking Research & Applications, Austin, TX, USA, March 22-24, pages 105–108, New York, NY, 2010. ACM. [More info]
• Samuel Kaski. Self-organizing maps. In Claude Sammut and Geoffrey I. Webb, editors, Encyclopedia of Machine Learning, pages 886–888. Springer, Berlin, 2010. [More info] [See also: dx.doi.org ...]
• Samuel Kaski. Three paths to relevance. In Akitoshi Hanazawa, Tsutom Miki, and Keiichi Horio, editors, Brain-Inspired Information Technology, pages 11–13. Springer, Berlin Heidelberg, 2010. [More info] [See also: dx.doi.org ...]
• Samuel Kaski, David J. Miller, Erkki Oja, and Antti Honkela, editors. Proceedings of the 2010 IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2010). IEEE, Piscataway, NJ, 2010. [More info]
• Arto Klami. Inferring task-relevant image regions from gaze data. In Samuel Kaski, David J. Miller, Erkki Oja, and Antti Honkela, editors, Proceedings of IEEE International Workshop on Machine Learning for Signal Processing (MLSP), pages 101–106. IEEE, 2010. [More info]
• Arto Klami, Seppo Virtanen, and Samuel Kaski. Bayesian exponential family projections for coupled data sources. In Peter Grunwald and Peter Spirtes, editors, Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence (2010), pages 286–293, Corvallis, Oregon, 2010. AUAI Press. PDF (421 kB) [More info]
• Leo Lahti, Juha E. A. Knuuttila, and Samuel Kaski. Global modeling of transcriptional responses in interaction networks. Bioinformatics, 26:2713–2720, 2010. Implementations in R and Matlab available at http://netpro.r-forge.r-project.org/. [More info] [See also: bioinformatics.oxfordjournals.org ...]
• Neda Mosakhani, Mohamed Guled, Leo Lahti, Ioana Borze, Minna Forsman, Jorma Ryhänen, and Sakari Knuutila. Unique microRNA profile in Dupuytren's contracture supports deregulation of beta-catenin pathway. Modern Pathology, 23:1544–1522, 2010. [More info] [See also: www.nature.com ...]
• Juuso Parkkinen and Samuel Kaski. Searching for functional gene modules with interaction component models. BMC Systems Biology, 4:4, 2010. [More info] [See also: www.biomedcentral.com ...]
• Juuso Parkkinen, Kristian Nybo, Jaakko Peltonen, and Samuel Kaski. Graph visualization with latent variable models. In Proceedings of MLG-2010, the Eighth Workshop on Mining and Learning with Graphs, pages 94–101, New York, NY, USA, 2010. ACM. DOI: http://doi.acm.org/10.1145/1830252.1830265. [More info] [See also: doi.acm.org ...]
• Jaakko Peltonen, Yusuf Yaslan, and Samuel Kaski. Relevant subtask learning by constrained mixture models. Intelligent Data Analysis, 14:641–662, 2010. [More info] [See also: dx.doi.org ...]
• Jaakko Peltonen, Helena Aidos, Nils Gehlenborg, Alvis Brazma, and Samuel Kaski. An information retrieval perspective on visualization of gene expression data with ontological annotation. In Proceedings of ICASSP 2010, IEEE International Conference on Acoustics, Speech and Signal Processing, pages 2178–2181, Piscataway, NJ, 2010. IEEE. PDF (205 kB) [More info]
• Jaakko Peltonen and Samuel Kaski. Generative modeling for maximizing precision and recall in information visualization. Technical Report TKK-ICS-R38, Aalto University, Department of Information and Computer Science, Espoo, Finland, 2010. PDF (5 MB) [More info]
• Simon Rogers, Arto Klami, Janne Sinkkonen, Mark Girolami, and Samuel Kaski. Infinite factorization of multiple non-parametric views. Machine Learning, 79(1-2):201–226, 2010. [More info] [See also: dx.doi.org ...]
• Abhishek Tripathi, Arto Klami, and Sami Virpioja. Bilingual sentence matching using kernel CCA. In Samuel Kaski, David J. Miller, Erkki Oja, and Antti Honkela, editors, Proceedings of IEEE International Workshop on Machine Learning for Signal Processing (MLSP), pages 130–135. IEEE, 2010. [More info]
• Jarkko Venna, Jaakko Peltonen, Kristian Nybo, Helena Aidos, and Samuel Kaski. Information retrieval perspective to nonlinear dimensionality reduction for data visualization. Journal of Machine Learning Research, 11:451–490, 2010. [More info] [See also: jmlr.csail.mit.edu ...]
• Jaakko Viinikanoja, Arto Klami, and Samuel Kaski. Variational Bayesian mixture of robust CCA models. In Aristides Gionis José Luis Balcázar, Francesco Bonchi and Michèle Sebag, editors, Machine Learning and Knowledge Discovery in Databases. Proceedings of European Conference, ECML PKDD 2010, Barcelona, Spain, September 20-24, 2010, volume III, pages 370–385, Berlin, 2010. Springer. Implementation in Matlab available at http://research.ics.tkk.fi/mi/software/vbcca/. [More info] [See also: www.springerlink.com ...]

  2009

• Antti Ajanki, Mark Billinghurst, Melih Kandemir, Samuel Kaski, Markus Koskela, Mikko Kurimo, Jorma Laaksonen, Kai Puolamäki, and Timo Tossavainen. Ubiquitous contextual information access with proactive retrieval and augmentation. Technical Report TKK-ICS-R27, Helsinki University of Technology, Department of Information and Computer Science, Espoo, Finland, December 2009. PDF (3 MB) [More info]
• Leo Lahti. RPA: probe reliability and differential gene expression analysis. BioConductor 2.5, October 2009. Computer program. [More info] [See also: bioconductor.org ...]
• Kristian Nybo, Juuso Parkkinen, and Samuel Kaski. Graph visualization with latent variable models. Technical Report TKK-ICS-R20, Helsinki University of Technology, Department of Information and Computer Science, Espoo, September 2009. [More info]
• Juuso Parkkinen, Janne Sinkkonen, Adam Gyenge, and Samuel Kaski. A block model suitable for sparse graphs. In Proceedings of the 7th International Workshop on Mining and Learning with Graphs (MLG 2009), Leuven, Belgium, July 2-4 2009. Extended Abstract. PDF (215 kB) [More info] [See also: www.cs.kuleuven.be ...]
• Eerika Savia, Kai Puolamäki, and Samuel Kaski. On two-way grouping by one-way topic models. Technical Report TKK-ICS-R15, Helsinki University of Technology, Department of Information and Computer Science, Espoo, May 2009. [More info]
• Antti Ajanki, David R. Hardoon, Samuel Kaski, Kai Puolamäki, and John Shawe-Taylor. Can eyes reveal interest?—Implicit queries from gaze patterns. User Modeling and User-Adapted Interaction: The Journal of Personalization Research, 19:307–339, 2009. [More info] [See also: dx.doi.org ...]
• Farid Benachenhou, Patric Jern, Merja Oja, Göran Sperber, Vidar Blikstad, Panu Somervuo, Samuel Kaski, and Jonas Blomberg. Evolutionary conservation of orthoretroviral long terminal repeats (LTRs) and ab initio detection of single LTRs in genomic data. PLoS ONE, 4(4):e5179, 2009. [More info] [See also: dx.doi.org ...]
• José Caldas, Nils Gehlenborg, Ali Faisal, Alvis Brazma, and Samuel Kaski. Probabilistic retrieval and visualization of biologically relevant microarray experiments. Bioinformatics, 25:i145–i153, 2009. (ISMB/ECCB 2009). PDF (896 kB) [More info] [See also: bioinformatics.oxfordjournals.org ...]
• José Caldas, Nils Gehlenborg, Ali Faisal, Alvis Brazma, and Samuel Kaski. Probabilistic retrieval and visualization of biologically relevant microarray experiments. BMC Bioinformatics, 10:P1, 2009. Poster abstract for the 5th ISCB Student Council. Best poster award. PDF (167 kB) [More info] [See also: www.biomedcentral.com ...]
• Mohamed Guled, Leo Lahti, Pamela M. Lindholm, Kaisa Salmenkivi, Izhar Bagwan, Andrew G. Nicholson, and Sakari Knuutila. CDKN2A, NF2 and JUN Are Dysregulated Among Other Genes by miRNAs in Malignant Mesothelioma - a miRNA Microarray Analysis. Genes, Chromosomes and Cancer, 48(7):615–623, 2009. [More info] [See also: www3.interscience.wiley.com ...]
• Ilkka Huopaniemi, Tommi Suvitaival, Janne Nikkilä, Matej Orešic, and Samuel Kaski. Two-way analysis of high-dimensional collinear data. Data Mining and Knowledge Discovery, 19:261–276, 2009. [More info] [See also: dx.doi.org ...]
• Ilkka Huopaniemi, Tommi Suvitaival, Janne Nikkilä, Matej Orešic, and Samuel Kaski. Multi-way, multi-view learning. In NIPS 2009 workshop on Learning from Multiple Sources with Applications to Robotics, 2009. Extended Abstract. PDF (413 kB) [More info]
• László Kozma, Arto Klami, and Samuel Kaski. GaZIR: Gaze-based zooming interface for image retrieval. In Proceedings of ICMI-MLMI 2009, The Eleventh International Conference on Multimodal Interfaces and The Sixth Workshop on Machine Learning for Multimodal Interaction, pages 305–312, New York, NY, USA, 2009. ACM. PDF (391 kB) [More info]
• Leo Lahti, Samuel Myllykangas, Sakari Knuutila, and Samuel Kaski. Dependency detection with similarity constraints. In Proceedings of MLSP 2009, IEEE International Workshop on Machine Learning for Signal Processing, pages 89–94. IEEE, 2009. PDF (218 kB) [More info] [See also: dx.doi.org ...]
• Gayle Leen, David R. Hardoon, and Samuel Kaski. Automatic choice of control measurements. In Z.-H. Zhou and T. Washio, editors, Advances in Machine Learning (Proc. ACML'09, The 1st Asian Conference on Machine Learning), volume 5828 of Lecture Notes in Computer Science, pages 206–219. Springer, 2009. [More info]
• Jaakko Peltonen, Jarkko Venna, and Samuel Kaski. Visualizations for assessing convergence and mixing of Markov chain Monte Carlo simulations. Computational Statistics and Data Analysis, 53:4453–4470, 2009. [More info] [See also: dx.doi.org ...]
• Kitsuchart Pasupa, Craig Saunders, Sandor Szedmak, Arto Klami, Samuel Kaski, and Steve Gunn. Learning to rank images from eye movements. In IEEE International Workshop on Human-Computer Interaction (HCI2009), October 4, 2009, Kyoto, Japan, pages 2009–2016, 2009. [More info]
• Jaakko Peltonen, Helena Aidos, and Samuel Kaski. Supervised nonlinear dimensionality reduction by neighbor retrieval. In Proceedings of ICASSP 2009, the IEEE International Conference on Acoustics, Speech, and Signal Processing, pages 1809–1812. IEEE, 2009. [More info] [See also: dx.doi.org ...]
• Jaakko Peltonen. Visualization by linear projections as information retrieval. In José Príncipe and Risto Miikkulainen, editors, Advances in Self-Organizing Maps (proceedings of WSOM 2009), pages 237–245, Berlin Heidelberg, 2009. Springer. [More info] [See also: dx.doi.org ...]
• Kai Puolamäki and Samuel Kaski. Bayesian solutions to the label switching problem. In N. Adams, C. Robardet, A. Siebes, and J.-F. Boulicaut, editors, Advances in Intelligent Data Analysis VIII, Proceedings of the 8th International Symposium on Intelligent Data Analysis, IDA 2009, pages 381–392, Berlin, 2009. Springer. PDF (716 kB) [More info] [See also: www.cis.hut.fi ...]
• Eerika Savia, Arto Klami, and Samuel Kaski. Fast dependent components for fMRI analysis. In Proceedings of ICASSP 09, the International Conference on Acoustics, Speech, and Signal Processing, pages 1737–1740. IEEE, 2009. PDF (247 kB) [More info]
• Eerika Savia, Kai Puolamäki, and Samuel Kaski. Two-way grouping by one-way topic models. In N. Adams, C. Robardet, A. Siebes, and J.-F. Boulicaut, editors, Advances in Intelligent Data Analysis VIII, Proceedings of the 8th International Symposium on Intelligent Data Analysis, IDA 2009, Lecture Notes in Computer Science, pages 178–189, Berlin, 2009. Springer. PDF (135 kB) [More info]
• Eerika Savia, Kai Puolamäki, and Samuel Kaski. Latent grouping models for user preference prediction. Machine Learning, 74:75–109, 2009. Published online: 3 September 2008. [More info] [See also: dx.doi.org ...]
• Suvi Savola, Arto Klami, Abhishek Tripathi, Tarja Niini, Massimo Serra, Piero Picci, Samuel Kaski, Diana Zambelli, Katia Scotlandi, and Sakari Knuutila. Combined use of expression and CGH arrays pinpoints novel candidate genes in ewing sarcoma family of tumors. BMC Cancer, 9:17, 2009. [More info] [See also: www.biomedcentral.com ...]
• Abhishek Tripathi, Arto Klami, and Samuel Kaski. Using dependencies to pair samples for multi-view learning. In Proceedings of ICASSP 09, the International Conference on Acoustics, Speech, and Signal Processing, pages 1561–1564. IEEE, 2009. PDF (90 kB) [More info]
• Keisuke Yamazaki and Samuel Kaski. An analysis of generalization error in relevant subtask learning. In Mario K?pen, Nikola Kasabov, and George Coghill, editors, Advances in Neuro-Information Processing, 15th International Conference, ICONIP 2008, pages 629–637, Berlin Heidelberg, 2009. Springer-Verlag. [More info] [See also: www.springerlink.com ...]
• Jarkko Ylipaavalniemi, Eerika Savia, Sanna Malinen, Riitta Hari, Ricardo Vigário, and Samuel Kaski. Dependencies between stimuli and spatially independent fMRI sources: Towards brain correlates of natural stimuli. NeuroImage, 48:176–185, 2009. [More info] [See also: dx.doi.org ...]

2008

• Gayle Leen and Colin Fyfe. Learning shared and separate features of two related data sets using GPLVM's. Poster in the NIPS 2008 Learning from Multiple Sources Workshop, December 13, Whistler, Canada. (pdf extended abstract)
• Jaakko Peltonen, Yusuf Yaslan, and Samuel Kaski. Variational Bayes Learning from Relevant Tasks Only. Poster in the NIPS 2008 Learning from Multiple Sources Workshop, December 13, Whistler, Canada. (abstract, pdf extended abstract)
• Janne Sinkkonen, Juuso Parkkinen, Janne Aukia and Samuel Kaski. A simple infinite topic mixture for rich graphs and relational data. Poster in the NIPS 2008 Workshop on Analyzing Graphs: Theory and Applications, December 12, Whistler, Canada. (abstract, pdf extended abstract)
• Simon Rogers, Janne Sinkkonen, Arto Klami, Mark Girolami, and Samuel Kaski. Two-level infinite mixture for multi-domain data. In the NIPS 2008 Workshop on Learning from Multiple Sources, 2008. (extended abstract).
• Arto Klami and Samuel Kaski. Probabilistic approach to detecting dependencies between data sets. Neurocomputing, 72:1-3, pp. 39-46, 2008. (abstract, DOI).
• José Caldas and Samuel Kaski. Bayesian biclustering with the plaid model. In proceedings of the IEEE International Workshop on Machine Learning for Signal Processing XVIII (MLSP), Cancún, Mexico, pages 291-296, 2008. (html)
• Eerika Savia, Kai Puolamäki and Samuel Kaski. Latent Grouping Models for User Preference Prediction. Machine Learning, 2008. (abstract, DOI).
• Arto Klami. Modeling of Mutual Dependencies. D.Sc. thesis. Dissertations in Information and Computer Science, Report D6. Espoo, Finland, 2008.
• Janne Sinkkonen, Janne Aukia and Samuel Kaski. Infinite mixtures for multi-relational categorical data. In MLG 2008, The 6th International Workshop on Mining and Learning with Graphs, Helsinki, July 4-5, 2008. (pdf).
• Kai Puolamäki, Antti Ajanki, and Samuel Kaski: Learning to Learn Implicit Queries from Gaze Patterns. International Conference on Machine Learning (ICML 2008), Helsinki, Finland, July 5-9, 2008. (abstract, pdf)
• Abhishek Tripathi, Arto Klami and Samuel Kaski. Simple integrative preprocessing preserves what is shared in data sources. BMC Bioinformatics, 2008,9:111.(html)
• Janne Nikkilä, Marko Sysi-Aho, Andrey Ermolov, Tuulikki Seppänen-Laakso, Olli Simell, Samuel Kaski and Matej Orešič. Gender-dependent progression of systemic metabolic states in early childhood. Molecular Systems Biology, 2008,4:197.(html)
• Arto Klami, Craig Saunders, Teofilo de Campos, and Samuel Kaski. Can relevance of images be inferred from eye movements?. MIR'08: Proceedings of the 1st ACM International Conference on Multimedia Information Retrieval 2008.(abstract, pdf)

2007

• Janne Aukia, Samuel Kaski and Janne Sinkkonen. Inferring vertex properties from topology in large networks. Poster in the NIPS 2007 Workshop on Statistical Network Models, December 8, Whistler, Canada. (pdf extended abstract, poster)
• Juuso Parkkinen and Samuel Kaski. Searching for functional gene modules with interaction component models. Presentation in the NIPS 2007 Workshop on Machine Learning in Computational Biology, December 7, Whistler, Canada. (abstract, pdf extended abstract)
• Samuel Kaski and Jaakko Peltonen. Learning from Relevant Tasks Only. In Joost N. Kok, Jacek Koronacki, Ramon Lopez de Mantaras, Stan Matwin, Dunja Mladenic, and Andrzej Skowron, editors, Machine Learning: ECML 2007 (Proceedings of the 18th European Conference on Machine Learning), Lecture Notes in Artificial Intelligence 4701, pages 608-615. Springer-Verlag, Berlin, Germany, 2007. (abstract, preprint pdf, final paper on Springer pages) ©2007 Springer-Verlag.
• Jaakko Peltonen, Jacob Goldberger, and Samuel Kaski. Fast Semi-supervised Discriminative Component Analysis. In Konstantinos Diamantaras, Tžlay Adali, Ioannis Pitas, Jan Larsen, Theophilos Papadimitriou, and Scott Douglas, editors, Machine Learning for Signal Processing XVII, pages 312-317. IEEE, 2007. (abstract, preprint pdf)
• Janne Sinkkonen, Janne Aukia and Samuel Kaski. Inferring vertex properties from topology in large networks. In MLG'07, The 5th International Workshop on Mining and Learning with Graphs, Firenze, Aug 1-3, 2007. (pdf).
• Jarkko Venna. Dimensionality Reduction for Visual Exploration of Similarity Structures. D.Sc. thesis. Dissertations in Computer and Information Science, Report D20. Espoo, Finland, 2007.
• Kristian Nybo, Jarkko Venna and Samuel Kaski. The self-organizing map as a visual neighbor retrieval method. In Proceedings of 6th Int. Workshop on Self-Organizing Maps (WSOM '07). Bielefeld University, Bielefeld, Germany, 2007. (pdf)
• Arto Klami and Samuel Kaski. Local Dependent Components. In Zoubin Ghahramani (Ed.), Proceedings of the 24th International Conference on Machine Learning (ICML 2007), pp. 425-433. Omni Press, 2007. (abstract, pdf)
• Jarkko Ylipaavalniemi, Eerika Savia, Ricardo Vig·rio and Samuel Kaski. Functional Elements and Networks in fMRI. Proceedings of the 15th European Symposium on Artificial Neural Networks (ESANN 2007), pages 561-566, Bruges, Belgium, April 2007. (abstract, pdf)
• Jarkko Venna and Samuel Kaski. Nonlinear Dimensionality Reduction as Information Retrieval. In Marina Meila and Xiaotong Shen, editors, Proceedings of AISTATS 2007, the 11th International Conference on Artificial Intelligence and Statistics. Omnipress, 2007. JMLR Workshop and Conference Proceedings, Volume 2: AISTATS 2007. (abstract, pdf)
• Jarkko Venna, and Samuel Kaski. Comparison of visualization methods for an atlas of gene expression data sets. Information Visualization, 6:139-154, 2007. (abstract, preprint pdf)
• David R. Hardoon, John Shawe-Taylor, Antti Ajanki, Kai Puolamäki, and Samuel Kaski: Information Retrieval by Inferring Implicit Queries from Eye Movements. In Marina Meila and Xiaotong Shen, editors, Proceedings of AISTATS 2007, the 11th International Conference on International Conference on Artificial Intelligence and Statistics. Omnipress, 2007. JMLR Workshop and Conference Proceedings, Volume 2: AISTATS 2007. (abstract, pdf)

2006

• Jarkko Venna and Samuel Kaski. Nonlinear dimensionality reduction viewed as information retrieval. Poster in the NIPS 2006 workshop on Novel Applications of Dimensionality Reduction, December 9, Whistler, Canada. (abstract, pdf extended abstract, pdf poster in A0 size)
• Jaakko Peltonen and Samuel Kaski. Learning when only some of the training data are from the same distribution as test data. Poster in the NIPS 2006 workshop on Learning when test and training inputs have different distributions, December 9, Whistler, Canada. (abstract, pdf extended abstract, pdf poster in A0 size)
• Jaakko Peltonen, Jacob Goldberger, and Samuel Kaski. Fast Discriminative Component Analysis for Comparing Examples. In NIPS 2006 workshop on Learning to Compare Examples, December 8, Whistler, Canada. (abstract, pdf)
• Arto Klami and Samuel Kaski. Generative models that discover dependencies between data sets. In S. McLoone, T. Adali, J. Larsen, M. Van Hulle, A. Rogers, S.C. Douglas, editors, Machine Learning for Signal Processing XVI, pages 123-128. IEEE, 2006. (abstract, preprint pdf)
• Jarkko Venna and Samuel Kaski. Local multidimensional scaling. Neural Networks, 19, pp 889--899, 2006. (abstract, preprint pdf)
• Jarkko Venna and Samuel Kaski. Visualizing Gene Interaction Graphs with Local Multidimensional Scaling. In Michel Verleysen, editor, Proceedings of the 14th European Symposium on Artificial Neural Networks (ESANN'2006), pages 557--562, Bruges, 2006. (abstract, preprint pdf)

2005

• Samuel Kaski. From learning metrics towards dependency exploration. In Proceedings of WSOM'05, 5th Workshop On Self-Organizing Maps, pages 307--314. Paris, 2005. (abstract, preprint pdf; a summary of underlying motivations)
• Jarkko Venna and Samuel Kaski. Local multidimensional scaling with controlled tradeoff between trustworthiness and continuity. In Proceedings of the 5th Workshop on Self-Organizing Maps (WSOM'2005), pages. 695--702, Paris, 2005(abstract, preprint pdf)
• Samuel Kaski, Janne Nikkilä, Janne Sinkkonen, Leo Lahti, Juha Knuuttila, and Christophe Roos. Associative clustering for exploring dependencies between functional genomics data sets. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2:203-216, 2005. (abstract, preprint pdf, ps, gzipped ps; the most thorough description of associative clustering, including three bioinformatics case studies)
• Kai Puolamäki, Jarkko Salojärvi, Eerika Savia, Jaana Simola and Samuel Kaski. Combining Eye Movements and Collaborative Filtering for Proactive Information Retrieval. In Gary Marchionini, Alistair Moffat, John Tait, Ricardo Baeza-Yates and Novio Ziviani, editors, Proceedings of SIGIR 2005, Twenty-Eighth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 146-153. ACM, 2005. (abstract, pdf)
• J. Salojärvi, K. Puolamäki, S. Kaski: Expectation Maximization Algorithms for Conditional Likelihoods . In Luc De Raedt and Stefan Wrobel, editors, Proceedings of the 22nd International Conference on Machine Learning (ICML 2005), pp. 753-760. ACM press, New York, USA. 2005. (abstract, pdf).
• E. Savia, K. Puolamäki, J. Sinkkonen and S. Kaski: Two-Way Latent Grouping Model for User Preference Prediction . In: Fahiem Bacchus and Tommi Jaakkola, editors, Proceedings of the 21st Conference on Uncertainty in Artificial Intelligence (UAI 2005), pp. 518-525. AUAI press, Corvallis, Oregon, USA. 2005. (abstract, pdf).
• J. Salojärvi, K. Puolamäki, S. Kaski: On Discriminative Joint Density Modeling. In: Gama, Camacho, Brazdil, Jorge, Torgo (eds.): Machine Learning: ECML 2005. (Proceedings of 16th European Conference on Machine Learning), Lecture Notes in Artificial Intelligence 3270, pages 341-352. Springer-Verlag, Berlin, Germany. 2005. (abstract, pdf,DOI).
• Janne Nikkilä, Christophe Roos, and Samuel Kaski. Integration of transcription factor binding and gene expression by associative clustering. In Proceedings of Symposium of Knowledge Representation in Bioinformatics. Espoo, Finland, 15.-17. June 2005. To appear.
• Janne Sinkkonen, Samuel Kaski Janne Nikkilä, and Leo Lahti. Associative Clustering (AC): Technical Details. Technical Report A84, Helsinki University of Technology, Publications in Computer and Information Science, Espoo, Finland, April 2005. (ps, pdf; accompanying report including only additional technical details and derivations of the method.)
• Jarkko Venna, and Samuel Kaski. Visualized atlas of a gene expression databank In Proceedings of Symposium of Knowledge Representation in Bioinformatics. Espoo, Finland, 15.-17. June 2005. (abstract, pdf)
• Arto Klami and Samuel Kaski. Non-parametric dependent components. In Proceedings of ICASSP'05, IEEE International Conference on Acoustics, Speech, and Signal Processing, pages V-209 - V-212, IEEE, 2005. (abstract, pdf; a generalization of canonical correlation analysis for non-Gaussian data)
• Samuel Kaski, Janne Sinkkonen, and Arto Klami. Discriminative clustering. Neurocomputing, 69:18-41, 2005. (preprint abstract, preprint pdf; publisher's site; the most comprehensive presentation on DC)
• Samuel Kaski, Janne Nikkilä, Eerika Savia, and Christophe Roos. Discriminative clustering of yeast stress response. In Udo Seiffert, Lakhmi Jain, and Patric Schweizer, editors, Bioinformatics using Computational Intelligence Paradigms, pages 75-92. Springer, Berlin, 2005. (preprint abstract, preprint pdf; bioinformatics application of DC, originally submitted in 2003)
• Jaakko Peltonen and Samuel Kaski. Discriminative Components of Data. IEEE Transactions on Neural Networks, 16:68-83, 2005. (preprint abstract, preprint pdf, final paper on IEEE pages) (A generalization of linear discriminant analysis for data visualization.)

2004

• Jaakko Peltonen, Arto Klami, and Samuel Kaski. Improved Learning of Riemannian Metrics for Exploratory Analysis. Neural Networks, vol. 17, pages 1087-1100, 2004. (preprint abstract, preprint gzipped postscript, Elsevier page linking the final paper, erratum to final paper on Elsevier pages) (Review of theory; better distance approximations; and application to self-organizing maps and Sammon's mapping) © Elsevier Ltd.
• Jaakko Peltonen. Data Exploration with Learning Metrics. D.Sc. thesis. Dissertations in Computer and Information Science, Report D7. Espoo, Finland, 2004.
• Janne Sinkkonen, Janne Nikkilä, Leo Lahti, and Samuel Kaski. Associative Clustering. In: Boulicaut, Esposito, Giannotti,Pedreschi (eds.): Machine Learning: ECML2004 ( Proceedings of 15th European Conference on Machine Learning), Lecture Notes in Computer Science 3201, pages 396-406, 2004. (abstract, pdf) (Clustering continuous data by dependency between two sets, with applications to gene homology detection.)
• Jaakko Peltonen, Janne Sinkkonen, and Samuel Kaski. Sequential Information Bottleneck for Finite Data. In: Russ Greiner and Dale Schuurmans, editors, Proceedings of the Twenty-First International Conference on Machine Learning (ICML 2004), pp. 647-654, Omnipress, Madison, WI, 2004. (abstract, pdf) (Finite-data version of Sequential Information Bottleneck, based on a Bayes factor.)
• Samuel Kaski and Janne Sinkkonen. Principle of learning metrics for data analysis. Journal of VLSI Signal Processing, special issue on Machine Learning for Signal Processing, vol 37, pp. 177-188. (abstract, postscript (draft), gzipped postscript (draft), Kluwer page linking the PDF) © Kluwer (Overview, some new asymptotic theory, and sketches of new directions. Note: sumbitted in 2002)

2003

• Jaakko Peltonen, Janne Sinkkonen, and Samuel Kaski. Finite Sequential Information Bottleneck (fsIB). Technical Report A74, Helsinki University of Technology, Publications in Computer and Information Science, Espoo, Finland, December 2003. (postscript, gzipped postscript) (Longer preliminary version of the ICML paper, containing some proofs omitted from it for brevity.)
• Janne Sinkkonen. Learning Metrics and Discriminative Clustering. PhD thesis. Dissertations on Computer and Information Science, report D2. Espoo, Finland, 2003.
• Samuel Kaski, Janne Nikkilä, Merja Oja, Jarkko Venna, Petri Törönen, and Eero Castren. Trustworthiness and metrics in visualizing similarity of gene expression. BMC Bioinformatics, 4:48, 2003. (Includes an application of learning metrics.)
• Jarkko Salojärvi, Ilpo Kojo, Jaana Simola and Samuel Kaski. Can relevance be inferred from eye movements in information retrieval ? In Proceedings of the Workshop on Self-Organizing Maps (WSOM'03), Hibikino, Kitakyushu, Japan, September 2003. pp. 261-266. (abstract,postscript,gzipped postscript) (Includes an application of learning metrics.)
• Jaakko Peltonen, Arto Klami and Samuel Kaski. Learning Metrics for Information Visualization. In Proceedings of the Workshop on Self-Organizing Maps (WSOM'03), Hibikino, Kitakyushu, Japan, September 2003. pp. 213-218. (abstract,postscript, gzipped postscript) (An extension of previous work, with a new distance computation algorithm applicable to e.g. Sammon's mapping.)
• Janne Sinkkonen, Janne Nikkilä, Leo Lahti and Samuel Kaski. Associative Clustering by Maximizing a Bayes Factor. Technical Report A68, Helsinki University of Technology, Publications in Computer and Information Science, Espoo, Finland, June 2003. (postscript, gzipped postscript) (Extension of DC to clustering of both margins of continuous co-occurrence data.)
• Samuel Kaski. Discriminative clustering. In Bulletin of the International Statistical Institute. Invited Paper Proceedings of the 54th Session, volume 2, pages 270-273. International Statistical Institute, 2003. (abstract, postscript, gzipped postscript, pdf) (Overview of our recent work on DC.)
• Samuel Kaski and Jaakko Peltonen. Informative discriminant analysis. In: Tom Fawcett and Nina Mishra, editors, Proceedings of the Twentieth International Conference on Machine Learning (ICML-2003), pp. 329-336, AAAI Press, Menlo Park, CA, 2003. (abstract,postscript, gzipped postscript, pdf) (A generalization of linear discriminant analysis for data visualization.)
• Samuel Kaski, Janne Sinkkonen, and Arto Klami. Regularized Discriminative Clustering. In C. Molina, T. Adali, J. Larsen, M. Van Hulle, editors, Neural Networks for Signal Processing XIII, pages 289-298. IEEE, New York, NY, 2003. (abstract,postscript, gzipped postscript, pdf) (Regularization and a tunable compromise between K-means and DC.)
• Jarkko Salojärvi, Samuel Kaski and Janne Sinkkonen. Discriminative clustering in Fisher metrics. In: O. Kaynak, E. Alpaydin, E. Oja, L. Xu, editors, Artificial Neural Networks and Neural Information Processing - Supplementary proceedings ICANN/ICONIP 2003,  Istanbul, Turkey, June, pp. 161-164. (abstract,postscript, gzipped postscript) (A method to improve clustering results of DC, motivated by the asymptotic connection to Fisher or learning metrics.)
• Jarkko Venna, Samuel Kaski and Jaakko Peltonen. Visualizations for Assessing Convergence and Mixing of MCMC. N. Lavrac, D. Gamberger, H. Blockeel, L. Todorovski, Editors,Proceedings of the 14th European Conference on Machine Learning (ECML 2003), pp. 432-443. Springer, Berlin, 2003. ( abstract,postscript, gzipped postscript) (A method to visually analyze MCMC simulations)
• Jarkko Venna and Samuel Kaski. Visualizing high-dimensional posterior distributions in Bayesian modeling. In: O. Kaynak, E. Alpaydin, E. Oja, L. Xu, editors, Artificial Neural Networks and Neural Information Processing - Supplementary proceedings ICANN/ICONIP 2003,  Istanbul, Turkey, June, pp. 165-168. (abstract,postscript,gzipped postscript) (A method to visualize high-dimensional posterior distributions using self-organizing maps in Fisher metric.)

2002

• Jaakko Peltonen, Janne Sinkkonen, and Samuel Kaski. Discriminative clustering of text documents. In: Lipo Wang, Jagath C. Rajapakse, Kunihiko Fukushima, Soo-Young Lee, Xin Yao (eds.) Proceedings of ICONIP'02, 9th International Conference on Neural Information Processing, volume 4, pages 1956-1960. IEEE, Piscataway, NJ, 2002. (abstract,postscript, gzipped postscript) (An extension of discriminative clustering to textual data.)
• Jaakko Peltonen, Arto Klami, and Samuel Kaski. Learning More Accurate Metrics for Self-Organizing Maps. In José R. Dorronsoro, editor, Artificial Neural Networks - ICANN 2002, International Conference, Madrid, Spain, August 2002, Proceedings, pp. 999-1004. Springer, 2002. (abstract,postscript, gzipped postscript) © Springer-Verlag (Improved estimates and approximations for Self-Organizing Maps that learn metrics, with more extensive testing.)
• Janne Sinkkonen, Samuel Kaski, and Janne Nikkilä. Discriminative Clustering: Optimal Contingency Tables by Learning Metrics. In: Tapio Elomaa, Heikki Mannila, Hannu Toivonen (eds.) Machine Learning: ECML 2002 (Proceedings of the ECML'02, 13th European Conference on Machine Learning), Lecture Notes in Artificial Intelligence 2430, Springer, Berlin, pp. 418-430, 2002. (abstract,postscript, gzipped postscript) © Springer-Verlag (Finite-data theory of DC. Also connects DC to learning metrics and introduces a new algorithm based on the generative interpretation.)
• Janne Sinkkonen and Samuel Kaski. Clustering based on conditional distributions in an auxiliary space. Neural Computation, 14:217-239, 2002. (abstract, postscript, gzipped postscript) (Infinite-data theory of DC.)

2001

• Samuel Kaski, Janne Sinkkonen, and Jaakko Peltonen. Bankruptcy analysis with self-organizing maps in learning metrics. IEEE Transactions on Neural Networks, 12:936-947, 2001. (preprint abstract, preprint postscript, preprint gzipped postscript, final paper on IEEE pages) (see also the ICANN'02 paper above.)
• Samuel Kaski, Janne Sinkkonen, and Jaakko Peltonen. Learning metrics for self-organizing maps. In Proceedings of IJCNN'01, International Joint Conference on Neural Networks, pages 914-919. IEEE, Piscataway, NJ, 2001. (abstract,postscript, gzipped postscript) (Short version of the previous paper)
• Samuel Kaski. Learning metrics for exploratory data analysis. In David Miller, Tulay Adali, Jan Larsen, Marc Van Hulle, and Scott Douglas, editors, Neural Networks for Signal Processing XI, Proceedings of the 2001 IEEE Signal Processing Society Workshop, pages 53-62. IEEE, New York, NY, 2001. (abstract, postscript,gzipped postscript) (Plenary, an overview)
• Samuel Kaski and Janne Sinkkonen. A topography-preserving latent variable model with learning metrics. In N. Allinson, H. Yin, L. Allinson, and J. Slack, editors, Advances in Self-Organizing Maps, pages 224-229. Springer, London, 2001. (abstract, postscript,gzipped postscript) (New preliminary work)
• Samuel Kaski, Janne Sinkkonen, and Janne Nikkilä. Clustering gene expression data by mutual information with gene function. In Georg Dorffner, Horst Bischof, and Kurt Hornik, editors, Artificial Neural Networks - ICANN 2001, pages 81-86. Springer, Berlin, 2001. (abstract, postscript,gzipped postscript) (Short version of the application in the 2001 Neural Computation paper)

2000

• Samuel Kaski. Convergence of a stochastic semisupervised clustering algorithm. Technical Report A62, Helsinki University of Technology, Publications in Computer and Information Science, Espoo, Finland, November 2000. (postscript,gzipped postscript) (Convergence proof for the Neural Computation 2002 paper)
• Janne Sinkkonen and Samuel Kaski. Clustering by similarity in an auxiliary space. In Proceedings of IDEAL 2000, Second International Conference on Intelligent Data Engineering and Automated Learning. Springer, 2000. In press. (abstract, postscript, gzipped postscript) (Earlier version of the algorithm in the 2001 Neural Computation paper)
• Samuel Kaski and Janne Sinkkonen. Metrics that learn relevance. In Proceedings of IJCNN-2000, International Joint Conference on Neural Networks, volume V, pages 547-552. IEEE Service Center, Piscataway, NJ, 2000. (abstract, postscript, gzipped postscript, errata) (First presentation of the ideas)
• Janne Sinkkonen and Samuel Kaski. Semisupervised clustering based on conditional distributions in an auxiliary space. Technical Report A60, Helsinki University of Technology, Publications in Computer and Information Science, Espoo, Finland, 2000. (abstract, postscript, gzipped postscript) (Earlier version of the 2001 Neural Computation paper, with an application to text documents)

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