Joint matrices decompositions and blind source separation: A survey of methods, identification, and applications

Gilles Chabriel, Martin Kleinsteuber, Eric Moreau, Hao Shen, Petr Tichavsky, Arie Yeredor

Research output: Contribution to journalReview articlepeer-review

120 Scopus citations

Abstract

Matrix decompositions such as the eigenvalue decomposition (EVD) or the singular value decomposition (SVD) have a long history in ?signal processing. They have been used in spectral analysis, signal/noise subspace estimation, principal component analysis (PCA), dimensionality reduction, and whitening in independent component analysis (ICA). Very often, the matrix under consideration is the covariance matrix of some observation signals. However, many other kinds of matrices can be encountered in signal processing problems, such as time-lagged covariance matrices, quadratic spatial time-frequency matrices [21], and matrices of higher-order statistics.

Original languageEnglish
Article number6784078
Pages (from-to)34-43
Number of pages10
JournalIEEE Signal Processing Magazine
Volume31
Issue number3
DOIs
StatePublished - May 2014

Funding

FundersFunder number
Czech Science Foundation102/09/1278

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