Sparse component analysis of overcomplete mixtures by improved basis pursuit method

Pando Georgiev, Andrzej Cichocki

Research output: Contribution to journalConference articlepeer-review

8 Citations (Scopus)

Abstract

We formulate conditions under which we can solve precisely the Blind Source Separation problem (BSS) in the under-determined case (less sensors than sources), up to permutation and scaling of sources. Under these conditions, which include information about sparseness of the sources (and hence we call the problem sparse component analysis (SCA)), we can 1) identify the mixing matrix uniquely (up to scaling and permutation) and 2) recover uniquely the original sources. We present a new algorithm for estimation of the mixing matrix, as well as an algorithm for SCA (estimation of sparse sources), which improves the standard basis pursuit method of S. Chen, D. Donoho and M. Sounders (when the mixing matrix is known or correctly estimated). Our methods are illustrated with examples.

Original languageEnglish
Pages (from-to)V-37-V-40
JournalProceedings - IEEE International Symposium on Circuits and Systems
Volume5
Publication statusPublished - 2004
Externally publishedYes
Event2004 IEEE International Symposium on Cirquits and Systems - Proceedings - Vancouver, BC, Canada
Duration: 23 May 200426 May 2004

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