Extraction of signals with specific temporal structure using kernel methods

Authors:
Eder Santana;Jose C. Principe;Ewaldo Eder Santana;Raimundo Carlos Silvério Freire;Allan Kardec Barros
Affiliations:
Department of Electrical Engineering, Federal University of Maranhão, São Luís, Maranhão, Brazil;Computational NeuroEngeneering Laboratory, Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL;State University of Maranhão, Maranhão, Brazil;Federal University of Campina Grande;National Agency of Petroleum, Gas and Biofuels, Rio de Janeiro, Brazil
Venue:
IEEE Transactions on Signal Processing
Year:
2010

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Abstract

This work derives and evaluates a method for Blind Source Extraction (BSE) in a reproducing kernel Hilbert space (RKHS) framework. The a priori information about the autocorrelation function of the signal under study is translated in a linear transformation of the Gram matrix of the transformed data in Hilbert space. Our method proved to be more robust than methods presented in the literature of BSE with respect to ambiguities in the available a priori information of the signal to be extracted. The approach here introduced can also be seen as a generalization of Kernel principal component analysis (KPCA) to analyze autocorrelation matrices at specific time lags.