Joint Matrices Decompositions and Blind Source Separation [A survey of methods, identification, and applications]

被引：108

作者：

Chabriel, Gilles

Kleinsteuber, Martin ^{[1
,2
,3
]}

Moreau, Eric ^{[4
]}

Shen, Hao ^{[5
]}

Tichavsky, Petr ^{[6
]}

Yeredor, Arie

机构：

[1] Australian Natl Univ, Canberra, ACT, Australia

[2] Univ Wurzburg, Wurzburg, Germany

[3] Tech Univ Munich, Dept Elect Engn & Informat Technol, D-80290 Munich, Germany

[4] Res Grp SIIM Signal & Images LSIS UMR CNRS 7296, Paris, France

[5] Tech Univ Munich, Inst Data Proc, D-80290 Munich, Germany

[6] Acad Sci Czech Republ, Inst Informat Theory & Automat, CR-18208 Prague, Czech Republic

来源：

IEEE SIGNAL PROCESSING MAGAZINE | 2014年 / 31卷 / 03期

关键词：

GENERAL COVARIANCE-STRUCTURES; CANONICAL DECOMPOSITION; DIAGONALIZATION; FRAMEWORK; ALGORITHMS; UNIQUENESS; MIXTURES;

D O I：

10.1109/MSP.2014.2298045

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.© 2014 IEEE.

引用

页码：34 / 43

页数：10

共 54 条

[1] SENSITIVITY ANALYSIS FOR THE PROBLEM OF MATRIX JOINT DIAGONALIZATION [J].