2012 |
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114 | Zhirong Yang, Tele Hao, Onur Dikmen, Xi Chen, and Erkki Oja. Clustering by nonnegative matrix factorization using graph random walk. In Advances in Neural Information Processing Systems 25 (NIPS2012), pages 1088–1096, Lake Tahoe, USA, 2012. |
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113 | Zhirong Yang, He Zhang, and Erkki Oja. Online projective nonnegative matrix factorization for large datasets. In Proceedings of 19th International Conference on Neural Information Processing (ICONIP 2012), pages 285–290, Doha, Qatar, 2012. Springer. |
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112 | He Zhang, Zhirong Yang, and Erkki Oja. Adaptive multiplicative updates for projective nonnegative matrix factorization. In Proceedings of 19th International Conference on Neural Information Processing (ICONIP 2012), pages 277–284, Doha, Qatar, 2012. Springer. |
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2011 |
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111 | Mats Sjöberg, Satoru Ishikawa, Markus Koskela, Jorma Laaksonen, and Erkki Oja. PicSOM experiments in TRECVID 2011. In Proceedings of the TRECVID 2011 Workshop, Gaithersburg, MD, USA, December 2011. Available online at http://www-nlpir.nist.gov/projects/tvpubs/tv.pubs.org.html. |
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110 | Mari-Sanna Paukkeri, Ilkka Kivimäki, Santosh Tirunagari, Erkki Oja, and Timo Honkela. Effect of dimensionality reduction on different distance measures in document clustering. In B.-L. Lu, L. Zhang, and J. Kwok, editors, ICONIP 2011, Part III, number 7064 in LNCS, pages 167–176. Springer–Verlag Berlin Heidelberg, Shanghai, China, November 2011. |
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109 | Mark van Heeswijk, Yoan Miche, Erkki Oja, and Amaury Lendasse. GPU-accelerated and parallelized ELM ensembles for large-scale regression. Neurocomputing, 74(16):2430–2437, September 2011. |
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108 | Zhirong Yang, He Zhang, Zhijian Yuan, and Erkki Oja. Kullback-leibler divergence for nonnegative for nonnegative matrix factorization. In Proceedings of 21st International Conference on Artificial Neural Networks, pages 14–17, Espoo, Finland, 2011. Springer. |
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107 | Zhirong Yang and Erkki Oja. Projective nonnegative matrix factorization based on alpha-divergence. Journal of Artificial Intelligence and Soft Computing Research, 1(1):7–16, 2011. |
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106 | He Zhang, Mats Sjöberg, Jorma Laaksonen, and Erkki Oja. A multimodal information collector for content-based image retrieval system. In Proceedings of 18th International Conference on Neural Information Processing (ICONIP 2011). Springer, 2011. |
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105 | Zhirong Yang and Erkki Oja. Unified development of multiplicative algorithms for linear and quadratic nonnegative matrix factorization. IEEE Transactions on Neural Networks, 22(12):1878–1891, 2011. |
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2010 |
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104 | Zhirong Yang, Zhanxing Zhu, and Erkki Oja. Automatic rank determination in projective nonnegative matrix factorization. In Proceedings of the 9th International Conference on Latent Variable Analysis and Signal Separation (LVA2010), volume 6365 of Lecture Notes in Computer Science, pages 514–521, Saint Malo, France, September 2010. Springer. |
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103 | Zhirong Yang, Chiwei Wang, and Erkki Oja. Multiplicative updates for t-sne. In Proceedings of the 20th IEEE International Workshop on Machine Learning For Signal Processing (MLSP2010), pages 19–23, Kittilä, August 2010. |
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102 | Dusan Sovilj, Tapani Raiko, and Erkki Oja. Extending self-organizing maps with uncertainty information of probabilistic pca. In Proceedings of the International Joint Conference on Neural Networks (IJCNN 2010), pages 1–7, Barcelona, Spain, July 2010. |
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101 | Erkki Oja and Zhirong Yang. Orthogonal nonnegative learning for sparse feature extraction and approximate combinatorial optimization. Frontiers of Electrical and Electronic Engineering in China, 5(3):261–273, 2010. |
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100 | Zhirong Yang and Erkki Oja. Linear and nonlinear projective nonnegative matrix factorization. IEEE Transactions on Neural Networks, 21(5):734–749, 2010. |
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99 | Heikki Järvinen, Petri Räisänen, Marko Laine, Johanna Tamminen, Alexander Ilin, Erkki Oja, Antti Solonen, and Heikki Haario. Estimation of ECHAM5 climate model closure parameters with adaptive MCMC. Atmospheric Chemistry and Physics Discussion, 10:11951–11973, 2010. |
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2009 |
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98 | Emilio Corchado, Xindong Wu, Erkki Oja, Alvaro Herrero, and Bruno Baruque, editors. Hybrid Artificial Intelligence Systems, 4th International Conference, HAIS 2009, Salamanca, Spain, June 10-12, 2009. Proceedings, volume 5572 of Lecture Notes in Computer Science. Springer, 2009. |
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97 | Mark van Heeswijk, Yoan Miche, Tiina Lindh-Knuutila, Peter A. J. Hilbers, Timo Honkela, Erkki Oja, and Amaury Lendasse. Adaptive ensemble models of extreme learning machines for time series prediction. In ICANN (2), pages 305–314, 2009. |
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96 | Zhirong Yang and Erkki Oja. Projective nonnegative matrix factorization with -divergence. In ICANN (1), pages 20–29, 2009. |
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2008 |
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95 | Erkki Oja. Oja learning rule. Scholarpedia, 3(3):3612, 2008. |
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94 | Petr Tichavský, Zbynek Koldovský, and Erkki Oja. Corrections to "performance analysis of the fastica algorithm and cramér-rao bounds for linear independent component analysis". IEEE Transactions on Signal Processing, 56(4):1715–1716, 2008. |
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2007 |
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93 | Amaury Lendasse, Erkki Oja, Olli Simula, and Michel Verleysen. Time series prediction competition: The cats benchmark. Neurocomputing, 70(13-15):2325–2329, 2007. |
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92 | Petr Tichavský, Zbynek Koldovský, and Erkki Oja. Speed and accuracy enhancement of linear ica techniques using rational nonlinear functions. In ICA, pages 285–292, 2007. |
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2006 |
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91 | Stefanos D. Kollias, Andreas Stafylopatis, Wlodzislaw Duch, and Erkki Oja, editors. Artificial Neural Networks - ICANN 2006, 16th International Conference, Athens, Greece, September 10-14, 2006. Proceedings, Part I, volume 4131 of Lecture Notes in Computer Science. Springer, 2006. |
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90 | Stefanos D. Kollias, Andreas Stafylopatis, Wlodzislaw Duch, and Erkki Oja, editors. Artificial Neural Networks - ICANN 2006, 16th International Conference, Athens, Greece, September 10-14, 2006. Proceedings, Part II, volume 4132 of Lecture Notes in Computer Science. Springer, 2006. |
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89 | Sergey Borisov, Alexander Ilin, Ricardo Vigário, and Erkki Oja. Comparison of bss methods for the detection of ıt lpha-activity components in eeg. In ICA, pages 430–437, 2006. |
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88 | Scott C. Douglas, Zhijian Yuan, and Erkki Oja. Average convergence behavior of the fastica algorithm for blind source separation. In ICA, pages 790–798, 2006. |
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87 | Alexander Ilin, Harri Valpola, and Erkki Oja. Extraction of components with structured variance. In IJCNN, pages 5110–5117, 2006. |
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86 | Alexander Ilin, Harri Valpola, and Erkki Oja. Exploratory analysis of climate data using source separation methods. Neural Networks, 19(2):155–167, 2006. |
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85 | Zbynek Koldovský, Petr Tichavský, and Erkki Oja. Efficient variant of algorithm fastica for independent component analysis attaining the cramér-rao lower bound. IEEE Transactions on Neural Networks, 17(5):1265–1277, 2006. |
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84 | Erkki Oja and Zhijian Yuan. The fastica algorithm revisited: Convergence analysis. IEEE Transactions on Neural Networks, 17(6):1370–1381, 2006. |
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83 | Jussi Pakkanen, Jukka Iivarinen, and Erkki Oja. The evolving tree-analysis and applications. IEEE Transactions on Neural Networks, 17(3):591–603, 2006. |
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82 | K. Raju, Tapani Ristaniemi, Juha Karhunen, and Erkki Oja. Jammer suppression in ds-cdma arrays using independent component analysis. IEEE Transactions on Wireless Communications, 5(1):77–82, 2006. |
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81 | Petr Tichavský, Zbynek Koldovský, and Erkki Oja. Performance analysis of the fastica algorithm and crame/spl acute/r-rao bounds for linear independent component analysis. IEEE Transactions on Signal Processing, 54(4):1189–1203, 2006. |
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2005 |
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80 | Wlodzislaw Duch, Janusz Kacprzyk, Erkki Oja, and Slawomir Zadrozny, editors. Artificial Neural Networks: Biological Inspirations - ICANN 2005, 15th International Conference, Warsaw, Poland, September 11-15, 2005, Proceedings, Part I, volume 3696 of Lecture Notes in Computer Science. Springer, 2005. |
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79 | Wlodzislaw Duch, Janusz Kacprzyk, Erkki Oja, and Slawomir Zadrozny, editors. Artificial Neural Networks: Formal Models and Their Applications - ICANN 2005, 15th International Conference, Warsaw, Poland, September 11-15, 2005, Proceedings, Part II, volume 3697 of Lecture Notes in Computer Science. Springer, 2005. |
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78 | Zhijian Yuan and Erkki Oja. Projective nonnegative matrix factorization for image compression and feature extraction. In SCIA, pages 333–342, 2005. |
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2004 |
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77 | Markus Koskela, Jorma Laaksonen, and Erkki Oja. Use of image subset features in image retrieval with self-organizing maps. In CIVR, pages 508–516, 2004. |
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76 | Markus Koskela, Jorma Laaksonen, and Erkki Oja. Entropy-based measures for clustering and som topology preservation applied to content-based image indexing and retrieval. In ICPR (2), pages 1005–1009, 2004. |
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75 | Jorma Laaksonen, Markus Koskela, and Erkki Oja. Class distributions on som surfaces for feature extraction and object retrieval. Neural Networks, 17(8-9):1121–1133, 2004. |
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74 | Erkki Oja. Applications of independent component analysis. In ICONIP, pages 1044–1051, 2004. |
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73 | Erkki Oja. Finding clusters and components by unsupervised learning. In SSPR/SPR, pages 1–15, 2004. |
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72 | Erkki Oja, Stefan Harmeling, and Luis B. Almeida. Independent component analysis and beyond. Signal Processing, 84(2):215–216, 2004. |
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71 | Timo Ojala, Markus Koskela, Esa Matinmikko, Mika Rautiainen, Jorma Laaksonen, and Erkki Oja. Task-based user evaluation of content-based image database browsing systems. In CIVR, pages 234–242, 2004. |
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70 | Erkki Oja and Mark D. Plumbley. Blind separation of positive sources by globally convergent gradient search. Neural Computation, 16(9):1811–1825, 2004. |
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69 | Jussi Pakkanen, Jukka Iivarinen, and Erkki Oja. The evolving tree - a novel self-organizing network for data analysis. Neural Processing Letters, 20(3):199–211, 2004. |
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68 | Zhijian Yuan and Erkki Oja. A fastica algorithm for non-negative independent component analysis. In ICA, pages 1–8, 2004. |
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2003 |
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67 | Okyay Kaynak, Ethem Alpaydin, Erkki Oja, and Lei Xu, editors. Artificial Neural Networks and Neural Information Processing - ICANN/ICONIP 2003, Joint International Conference ICANN/ICONIP 2003, Istanbul, Turkey, June 26-29, 2003, Proceedings, volume 2714 of Lecture Notes in Computer Science. Springer, 2003. |
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66 | Matti Aksela, Ramunas Girdziusas, Jorma Laaksonen, Erkki Oja, and Jari Kangas. Methods for adaptive combination of classifiers with application to recognition of handwritten characters. IJDAR, 6(1):23–41, 2003. |
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65 | Maria Funaro, Erkki Oja, and Harri Valpola. Independent component analysis for artefact separation in astrophysical images. Neural Networks, 16(3-4):469–478, 2003. |
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64 | Te-Won Lee, Jean-Francois Cardoso, Erkki Oja, and Shun ichi Amari. Introduction to special issue on independent components analysis. Journal of Machine Learning Research, 4:1175–1176, 2003. |
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2002 |
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63 | Sami S. Brandt, Jorma Laaksonen, and Erkki Oja. Statistical shape features for content-based image retrieval. Journal of Mathematical Imaging and Vision, 17(2):187–198, 2002. |
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62 | Markus Koskela, Jorma Laaksonen, and Erkki Oja. Implementing relevance feedback as convolutions of local neighborhoods on self-organizing maps. In ICANN, pages 981–986, 2002. |
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61 | Markus Koskela, Jorma Laaksonen, and Erkki Oja. Using mpeg-7 descriptors in image retrieval with self-organizing maps. In ICPR (2), pages 1049–1052, 2002. |
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60 | Markus Koskela, Jorma Laaksonen, and Erkki Oja. Mpeg-7 descriptors in content-based image retrieval with picsom system. In VISUAL, pages 247–258, 2002. |
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59 | Erkki Oja. Finding hidden factors using independent component analysis. In ECML, page 505, 2002. |
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58 | Erkki Oja. Independent component analisys. In HIS, page 3, 2002. |
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57 | Erkki Oja. Finding hidden factors using independent component analysis. In PKDD, page 488, 2002. |
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56 | Erkki Oja. Unsupervised learning in neural computation. Theor. Comput. Sci., 287(1):187–207, 2002. |
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2001 |
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55 | Matti Aksela, Jorma Laaksonen, Erkki Oja, and Jari Kangas. Application of adaptive committee classifiers in on-line character recognition. In ICAPR, pages 270–279, 2001. |
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54 | Matti Aksela, Jorma Laaksonen, Erkki Oja, and Jari Kangas. Rejection methods for an adaptive committee classifier. In ICDAR, pages 982–986, 2001. |
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53 | Visa Koivunen, Mihai Enescu, and Erkki Oja. Adaptive algorithm for blind separation from noisy time-varying mixtures. Neural Computation, 13(10):2339–2357, 2001. |
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52 | Jorma Laaksonen, Markus Koskela, Sami Laakso, and Erkki Oja. Self-organising maps as a relevance feedback technique in content-based image retrieval. Pattern Anal. Appl., 4(2-3):140–152, 2001. |
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51 | Timo Ojala, Kimmo Valkealahti, Erkki Oja, and Matti Pietikäinen. Texture discrimination with multidimensional distributions of signed gray-level differences. Pattern Recognition, 34(3):727–739, 2001. |
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50 | Vuokko Vuori, Jorma Laaksonen, Erkki Oja, and Jari Kangas. Speeding up on-line recognition of handwritten characters by pruning the prototype set. In ICDAR, pages 501–, 2001. |
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49 | Vuokko Vuori, Jorma Laaksonen, Erkki Oja, and Jari Kangas. Experiments with adaptation strategies for a prototype-based recognition system for isolated handwritten characters. IJDAR, 3(3):150–159, 2001. |
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2000 |
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48 | Sami S. Brandt, Jorma Laaksonen, and Erkki Oja. Statistical shape features in content-based image retrieval. In ICPR, pages 6062–6066, 2000. |
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47 | Aapo Hyvärinen and Erkki Oja. Independent component analysis: algorithms and applications. Neural Networks, 13(4-5):411–430, 2000. |
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46 | Markus Koskela, Jorma Laaksonen, Sami Laakso, and Erkki Oja. Evaluating the performance of content-based image retrieval systems. In VISUAL, pages 430–441, 2000. |
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45 | Jorma Laaksonen, Markus Koskela, Sami Laakso, and Erkki Oja. Picsom - content-based image retrieval with self-organizing maps. Pattern Recognition Letters, 21(13-14):1199–1207, 2000. |
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44 | Ricardo Vigário and Erkki Oja. Independence: a new criterion for the analysis of the electromagnetic fields in the global brain?. Neural Networks, 13(8-9):891–907, 2000. |
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43 | Vuokko Vuori, Jorma Laaksonen, Erkki Oja, and Jari Kangas. Controlling on-line adaptation of a prototype-based classifier for handwritten characters. In ICPR, pages 2331–2334, 2000. |
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1999 |
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42 | Xavier Giannakopoulos, Juha Karhunen, and Erkki Oja. An experimental comparison of neural algorithms for independent component analysis and blind separation. Int. J. Neural Syst., 9(2):99–114, 1999. |
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41 | Jorma Laaksonen, Matti Aksela, Erkki Oja, and Jari Kangas. Dynamically expanding context as committee adaptation method in on-line recognition of handwritten latin characters. In ICDAR, pages 796–799, 1999. |
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40 | Jorma Laaksonen, Markus Koskela, and Erkki Oja. Content-based image retrieval using self-organizing maps. In VISUAL, pages 541–548, 1999. |
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39 | Erkki Oja, Aapo Hyvärinen, and Patrik O. Hoyer. Image feature extraction and denoising by sparse coding. Pattern Anal. Appl., 2(2):104–110, 1999. |
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38 | Ricardo Vigário and Erkki Oja. Independent component analysis of human brain waves. In IWANN (2), pages 238–247, 1999. |
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37 | Vuokko Vuori, Jorma Laaksonen, Erkki Oja, and Jari Kangas. On-line adaptation in recognition of handwritten alphanumeric characters. In ICDAR, pages 792–795, 1999. |
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1998 |
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36 | Aapo Hyvärinen, Patrik O. Hoyer, and Erkki Oja. Sparse code shrinkage: Denoising by nonlinear maximum likelihood estimation. In NIPS, pages 473–479, 1998. |
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35 | Juha Karhunen, Petteri Pajunen, and Erkki Oja. The nonlinear pca criterion in blind source separation: Relations with other approaches. Neurocomputing, 22(1-3):5–20, 1998. |
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34 | Kimmo Kiviluoto and Erkki Oja. Independent component analysis for parallel financial time series. In ICONIP, pages 895–898, 1998. |
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33 | Jorma Laaksonen and Erkki Oja. Learning subspace classifiers and error-corrective feature extraction. IJPRAI, 12(4):423–436, 1998. |
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32 | Erkki Oja. Signal decomposition by fast ica. In ICONIP, pages 594–602, 1998. |
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31 | Erkki Oja. From neural learning to independent components. Neurocomputing, 22(1-3):187–199, 1998. |
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30 | Erkki Oja. The nonlinear pca approach to ica. In ICONIP, pages 725–728, 1998. |
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29 | Kimmo Valkealahti and Erkki Oja. Reduced multidimensional co-occurrence histograms in texture classification. IEEE Trans. Pattern Anal. Mach. Intell., 20(1):90–94, 1998. |
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28 | Kimmo Valkealahti and Erkki Oja. Texture classification with single- and multiresolution co-occurrence maps. IJPRAI, 12(4):437–452, 1998. |
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1997 |
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27 | Aapo Hyvärinen and Erkki Oja. A fast fixed-point algorithm for independent component analysis. Neural Computation, 9(7):1483–1492, 1997. |
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26 | Kimmo Kiviluoto and Erkki Oja. S-map: A network with a simple self-organization algorithm for generative topographic mappings. In NIPS, 1997. |
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25 | Erkki Oja. The nonlinear pca learning rule in independent component analysis. Neurocomputing, 17(1):25–45, 1997. |
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24 | Erkki Oja, Juha Karhunen, and Aapo Hyvärinen. From neural principal components to neural independent components. In ICANN, pages 519–528, 1997. |
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23 | Erkki Oja and Kimmo Valkealahti. Local independent component analysis by the self-organizing map. In ICANN, pages 553–558, 1997. |
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22 | Ricardo Vigário, Veikko Jousmäki, Matti Hämäläinen, Riitta Hari, and Erkki Oja. Independent component analysis for identification of artifacts in magnetoencephalographic recordings. In NIPS, 1997. |
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1996 |
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21 | Aapo Hyvärinen and Erkki Oja. One-unit learning rules for independent component analysis. In NIPS, pages 480–486, 1996. |
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20 | Aapo Hyvärinen and Erkki Oja. Simple neuron models for independent component analysis. Int. J. Neural Syst., 7(6):671–688, 1996. |
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19 | Heikki Kälviäinen, Petri Hirvonen, and Erkki Oja. Houghtool – a software package for the use of the hough transform. Pattern Recognition Letters, 17(8):889–897, 1996. |
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18 | Jorma Laaksonen and Erkki Oja. Subspace dimension selection and averaged learning subspace method in handwritten digit classification. In ICANN, pages 227–232, 1996. |
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17 | Erkki Oja and Kimmo Valkealahti. Co-occurrence map: Quantizing multidimensional texture histograms. Pattern Recognition Letters, 17(7):723–730, 1996. |
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16 | Erkki Oja and Liuyue Wang. Robust fitting by nonlinear neural units. Neural Networks, 9(3):435–444, 1996. |
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15 | Erkki Oja and Liuyue Wang. Neural fitting: Robustness by anti-hebbian learning. Neurocomputing, 12(2-3):155–170, 1996. |
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14 | Kimmo Valkealahti and Erkki Oja. Optimal texture feature selection for the co-occurrence map. In ICANN, pages 245–250, 1996. |
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1995 |
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13 | Heikki Kälviäinen, Petri Hirvonen, Lei Xu, and Erkki Oja. Probabilistic and non-probabilistic hough transforms: overview and comparisons. Image Vision Comput., 13(4):239–252, 1995. |
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1994 |
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12 | Heikki Kälviäinen, Petri Hirvonen, Lei Xu, and Erkki Oja. Comparisons of probabilistic and non-probabilistic hough transforms. In ECCV (2), pages 351–360, 1994. |
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1992 |
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11 | Jouko Lampinen and Erkki Oja. Clustering properties of hierarchical self-organizing maps. Journal of Mathematical Imaging and Vision, 2(2-3):261–272, 1992. |
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10 | Erkki Oja. Principal components, minor components, and linear neural networks. Neural Networks, 5(6):927–935, 1992. |
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9 | Lei Xu, Erkki Oja, and Ching Y. Suen. Modified hebbian learning for curve and surface fitting. Neural Networks, 5(3):441–457, 1992. |
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1991 |
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8 | Lei Xu, Adam Krzyzak, and Erkki Oja. Neural nets for dual subspace pattern recognition method. Int. J. Neural Syst., 2(3):169–184, 1991. |
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1990 |
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7 | Pekka Kultanen, Erkki Oja, and Lei Xu. Randomized hough transform (rht) in engineering drawing vectorization system. In MVA, pages 173–176, 1990. |
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6 | Jussi Parkkinen, K. Selkäinaho, and Erkki Oja. Detecting texture periodicity from the cooccurrence matrix. Pattern Recognition Letters, 11(1):43–50, 1990. |
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5 | Lei Xu and Erkki Oja. Improved simulated annealing, boltzmann machine, and attributed graph matching. In EURASIP Workshop, pages 151–160, 1990. |
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4 | Lei Xu, Erkki Oja, and Pekka Kultanen. A new curve detection method: Randomized hough transform (rht). Pattern Recognition Letters, 11(5):331–338, 1990. |
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1989 |
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3 | Erkki Oja. Neural networks, principal components, and subspaces. Int. J. Neural Syst., 1(1):61–68, 1989. |
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1983 |
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2 | Erkki Oja and Maija Kuusela. The alsm algorithm - an improved subspace method of classification. Pattern Recognition, 16(4):421–427, 1983. |
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1979 |
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1 | Erkki Oja. On the construction of projectors using products of elementary matrices. IEEE Trans. Computers, 28(1):65–66, 1979. |
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