Independent Component Analysis (ICA) and Blind Source Separation (BSS)
"Independent Component Analysis"
NEW! Advanced book
"Handbook of Blind Source Separation, Independent Component
Analysis and Applications"
Current and recent research projects (2006-2010)
You can find summaries with references on our current and recent research
projects related to independent component analysis (ICA) and blind source separation
- Denoising source separation
- Nonlinear ICA and BSS
- 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.
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.
report on our Bayes research in 2006-2007 includes Nonlinear BSS and ICA, Nonlinear
state-space models, and Non-negative blind source separation.
Free software developed by us
software package implements in various environments the fixed-point
algorithm. It is currently the most popular ICA algorithm because of its fast
operation and applicability to large-scale problems.
Software for investigating the reliability of ICA estimates by clustering
A Matlab package for
denoising source separation, which is a semi-blind source separation
technique based on denoising procedures.
Matlab packages for
Nonlinear factor analysis (NFA) and Nonlinear dynamical factor analysis
(NDFA). NFA can be used for nonlinear PCA and BSS, and NDFA is its
extension for blind identification of a nonlinear dynamic state-space model.
A Matlab package for
Projective nonnegative matrix factorization (PNMF).
Try-it-yourself demo: Cocktail party problem
Cocktailkutsu-demonstraatio sekoittuneiden äänten erottelusta
People involved in ICA and BSS research:
Prof. Aapo Hyvärinen,
Dr. Patrik Hoyer,
Dr. Markus Harva,
Dr. Ella Bingham,
Prof. Jyrki Joutsensalo,
Dr. Harri Valpola,
Dr. Jaakko Särelä,
Dr. Jarmo Hurri, Kimmo Kiviluoto, Dr. Mika Inki, Dr. Raju Karthikesh, Dr.
Petteri Pajunen, Simona Malaroiu, Razvan Cristescu, Tomas Östman.
Earlier research projects (2000-2005)
- FastICA algorithm
for independent component analysis
- ICA and its extensions
as models of natural image statistics
- Icasso: software for
stability analysis of independent components
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.
report on our Bayes research in 2004-2005 includes Nonlinear and non-negative
blind source separation, and Dynamic modelling using nonlinear state-space models.
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.
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.
report on our Bayes research in 2000-2001 includes Nonlinear factor analysis
and independent component analysis, Nonlinear dynamic state-space models, and
ICA link collection
Click here for a collection of program packages, data sets and links
Page maintained by webmaster at cis.hut.fi,
last updated Friday, 30-Apr-2010 11:05:03 EEST