A clustering approach for blind source separation with more sources than mixtures

Zhenwei Shi, Huanwen Tang, Yiyuan Tang

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

In this paper, blind source separation is discussed with more sources than mixtures when the sources are sparse. The blind separation technique includes two steps. The first step is to estimate a mixing matrix, and the second is to estimate sources. The mixing matrix can be estimated by using a clustering approach which is described by the generalized exponential mixture model. The generalized exponential mixture model is a powerful uniform framework to learn the mixing matrix for sparse sources. After the mixing matrix is estimated, the sources can be obtained by solving a linear programming problem. The techniques we present here can be extended to the blind separation of more sources than mixtures with a Gaussian noise.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsFuliang Yin, Chengan Guo, Jun Wang
PublisherSpringer-Verlag
Pages684-689
Number of pages6
ISBN (Print)3540228411, 9783540228417
DOIs
StatePublished - 2004

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3173
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

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