Second and higher-order correlation analysis of multiple multidimensional variables by joint diagonalization

Authors:
Xi-Lin Li;Matthew Anderson;Tülay Adali
Affiliations:
Machine Learning for Signal Processing Laboratory, University of Maryland Baltimore County, Baltimore, MD;Machine Learning for Signal Processing Laboratory, University of Maryland Baltimore County, Baltimore, MD;Machine Learning for Signal Processing Laboratory, University of Maryland Baltimore County, Baltimore, MD
Venue:
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
Year:
2010

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Abstract

In this paper, we introduce two efficient methods for second and higher-order correlation, or linear and nonlinear dependence, analysis of several multidimensional variables. We show that both the second and higher-order correlation analysis can be cast into a specific joint diagonalization problem. Compared with existing multiset canonical correlation analysis (MCCA) and independent vector analysis (IVA) algorithms, desired features of the new methods are that they can exploit the nonwhiteness of observations, they do not assume a specific density model, and they use simultaneous separation and thus are free of error accumulation arising in deflationary separation. Simulation results are presented to show the performance gain of the new methods over MCCA and IVA approaches.