Analysis and Online Realization of the CCA Approach for Blind Source Separation

Authors:
Wei Liu;D. P. Mandic;A. Cichocki
Affiliations:
Univ. of Sheffield, Sheffield;-;-
Venue:
IEEE Transactions on Neural Networks
Year:
2007

Citing 0
Cited 3

Joint blind source separation by multiset canonical correlation analysis

IEEE Transactions on Signal Processing
Temporally local maximum signal fraction analysis for artifact removal from biomedical signals

IEEE Transactions on Signal Processing
Finding dependent and independent components from related data sets: A generalized canonical correlation analysis based method

Neurocomputing

Quantified Score

Hi-index	0.01

Visualization

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.