TY - JOUR
T1 - Joint matrices decompositions and blind source separation
T2 - A survey of methods, identification, and applications
AU - Chabriel, Gilles
AU - Kleinsteuber, Martin
AU - Moreau, Eric
AU - Shen, Hao
AU - Tichavsky, Petr
AU - Yeredor, Arie
PY - 2014/5
Y1 - 2014/5
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85032752234&partnerID=8YFLogxK
U2 - 10.1109/MSP.2014.2298045
DO - 10.1109/MSP.2014.2298045
M3 - ???researchoutput.researchoutputtypes.contributiontojournal.systematicreview???
AN - SCOPUS:85032752234
SN - 1053-5888
VL - 31
SP - 34
EP - 43
JO - IEEE Signal Processing Magazine
JF - IEEE Signal Processing Magazine
IS - 3
M1 - 6784078
ER -