Combined independent component analysis and canonical polyadic decomposition via joint diagonalization

Recently, there has been a trend to combine independent component analysis and canonical polyadic decomposition (ICA-CPD) for an enhanced robustness for the computation of CPD, and ICA-CPD could be further converted into CPD of a 5th-order partially symmetric tensor, by calculating the eigenmatrices...

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Bibliographic Details
Published in2014 IEEE China Summit & International Conference on Signal and Information Processing (ChinaSIP) pp. 804 - 808
Main Authors Xiao-Feng Gong, Cheng-Yuan Wang, Ya-Na Hao, Qiu-Hua Lin
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2014
Subjects
Algorithm design and analysis
Blind source separation
Canonical polyadic decomposition
Independent component analysis
Joint diagonalization
Joints
Loading
Matrix decomposition
Signal to noise ratio
Tensile stress
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Summary:Recently, there has been a trend to combine independent component analysis and canonical polyadic decomposition (ICA-CPD) for an enhanced robustness for the computation of CPD, and ICA-CPD could be further converted into CPD of a 5th-order partially symmetric tensor, by calculating the eigenmatrices of the 4th-order cumulant slices of a trilinear mixture. In this study, we propose a new 5th-order CPD algorithm constrained with partial symmetry based on joint diagonalization. As the main steps involved in the proposed algorithm undergo no updating iterations for the loading matrices, it is much faster than the existing algorithm based on alternating least squares and enhanced line search, with competent performances. Simulation results are provided to demonstrate the performance of the proposed algorithm.
ISBN:9781479954018
1479954012
DOI:10.1109/ChinaSIP.2014.6889356