Research of the ICA and BSS group

You can find summaries with references on our current and recent research projects related to independent component analysis (ICA) and blind source separation (BSS) as well as to their extensions and applications here:

Non-negative low-rank learning

Finding dependent and independent components from two data sets

Research projects in 2008-2009

Non-negative projections

Gaussian process factor analysis of historical climate data

Nonlinear ICA and BSS

See also the Bayes Group and the Neuroinformatics Group

Biennial progress reports on our research

Progress report on our ICA and BSS research in 2008-2009: Introduction, Non-negative projections, and Reconstruction of historical climate data by Gaussian-process factor analysis.

Progress report on our ICA and BSS research in 2006-2007: Introduction, Convergence and finite-sample behaviour of the FastICA algorithm, Independent subspaces with decoupled dynamics, Extending ICA for two related data sets, ICA in CDMA communications, Non-negative projections, and Climate data analysis with DSS.

Progress report on our Bayes research in 2006-2007 includes Nonlinear BSS and ICA, Nonlinear state-space models, and Non-negative blind source separation.

Earlier research projects (2000-2005)

FastICA algorithm for independent component analysis

Denoising source separation

ICA and its extensions as models of natural image statistics

Icasso: software for stability analysis of independent components

Progress report on our ICA and BSS research in 2004-2005: Introduction, Finite sample behaviour of the FastICA algorithm, Nonlinear ICA and BSS, Denoising source separation, Climate data analysis with DSS, ICA and denoising source separation in CDMA communications, ICA for image representations, Analyzing 0-1 data.

Progress report on our Bayes research in 2004-2005 includes Nonlinear and non-negative blind source separation, and Dynamic modelling using nonlinear state-space models.

Progress report on our ICA and BSS research in 2002-2003: Introduction, Theoretical advances, Comparison studies on blind separation of post-nonlinear mixtures, Text mining, ICA for astronomical data, ICA in CDMA communications, Explorative investigation of the reliability of independent component estimates, The European joint project BLISS.

Progress report on our Bayes research in 2002-2003 includes Nonlinear static and dynamic blind source separation, Hierarchical modeling of variances, and Applications.

Progress report on our application of ICA in 2000-2001: Decision trees using independent component analysis, ICA for text mining, ICA for analyzing financial time series, ICA for astronomical data, and ICA in CDMA communications.

Progress report on our Bayes research in 2000-2001 includes Nonlinear factor analysis and independent component analysis, Nonlinear dynamic state-space models, and Applications.

Figure caption

Cultural centre, central tower and pool in Tapiola, Espoo, about 2 km from our department. Source of the figure: www.visitespoo.fi.