Title :
Analysis and Online Realization of the CCA Approach for Blind Source Separation
Author :
Liu, Wei ; Mandic, Danilo P. ; Cichocki, Andrzej
Author_Institution :
Univ. of Sheffield, Sheffield
Abstract :
A critical analysis of the canonical correlation analysis (CCA) approach in blind source separation (BSS) is provided. It is proved that by maximizing the autocorrelation functions of the recovered signals we can separate the source signals successfully. It is further shown that the CCA approach represents the same class of generalized eigenvalue decomposition (GEVD) problems as the matrix pencil method. Finally, online realizations of the CCA approach are discussed with a linear-predictor-based algorithm studied as an example.
Keywords :
blind source separation; correlation methods; eigenvalues and eigenfunctions; matrix algebra; autocorrelation functions; blind source separation; canonical correlation analysis; generalized eigenvalue decomposition problems; linear-predictor-based algorithm; matrix pencil method; recovered signals; Autocorrelation; Blind source separation; Covariance matrix; Eigenvalues and eigenfunctions; Higher order statistics; Matrix decomposition; Signal processing; Signal processing algorithms; Source separation; Statistical analysis; Blind source separation (BSS); canonical correlation analysis (CCA); linear predictor; matrix pencil; second-order statistics (SOS); Algorithms; Artificial Intelligence; Computer Simulation; Models, Statistical; Pattern Recognition, Automated; Regression Analysis;
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2007.894017
