Blind signal separation and extraction: Recent trends, future perspectives, and applications

Andrzej Cichocki, Jacek M. Zurada

Research output: Contribution to journalConference articlepeer-review

9 Citations (Scopus)

Abstract

Blind source separation (BSS) and related methods, e.g., ICA are generally based on a wide class of unsupervised learning algorithms and they found potential applications in many areas from engineering to psychology and neuroscience. The recent trends in BBS is to consider problems in the framework of probabilistic generative and tree structured graphical models and exploit a priori knowledge about true nature and structure of latent (hidden) variables or sources such as statistical independence, spatio-temporal decorrelation, sparseness, smoothness or linear predictability. The goal of BSS can be considered as estimation of sources and parameters of a mixing system or more generally as finding a new reduced or compressed representation for the observed (sensor) data that can be interpreted as physically meaningful coding or blind source extraction. The key issue is to find a such transformation or coding (linear or nonlinear) which has true physical meaning and interpretation. In this paper, we briefly review some promising linear models and approaches to blind source separation and extraction using various criteria and assumptions.

Original languageEnglish
Pages (from-to)30-37
Number of pages8
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3070
DOIs
Publication statusPublished - 2004
Externally publishedYes
Event7th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2004 - Zakopane, Poland
Duration: 7 Jun 200411 Jun 2004

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