Institute of Information Theory and Automation

Publication details

Joint Matrices Decompositions and Blind Source Separation

Journal Article

Chabriel G., Kleinsteuber M., Moreau E., Shen H., Tichavský Petr, Yeredor A.

serial: IEEE Signal Processing Magazine vol.31, 3 (2014), p. 34-43

project(s): GA102/09/1278, GA ČR

keywords: joint matrices decomposition, tensor decomposition, blind source separation

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abstract (eng):

This article provides a comprehensive survey of matrix joint decomposition techniques in the context of source separation. More precisely, we first intend to elaborate upon the signal models leading to different useful sets of matrices and their joint decompositions. Second, we present recent identifiability results and algorithms in distinguishing important classes.


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Last modification: 21.12.2012
Institute of Information Theory and Automation