Blind extraction of the sparsest component

Authors:
Everton Z. Nadalin;André K. Takahata;Leonardo T. Duarte;Ricardo Suyama;Romis Attux
Affiliations:
Department of Computer Engineering and Industrial Automation, University of Campinas, Campinas, SP, Brazil and Lab. of Signal Processing for Communications, School of Electrical and Computer Engin ...;Department of Communication, University of Campinas, Campinas, SP, Brazil and Lab. of Signal Processing for Communications, School of Electrical and Computer Engineering, University of Campinas, C ...;Department of Microwave and Optics, University of Campinas, Campinas, SP, Brazil and Lab. of Signal Processing for Communications, School of Electrical and Computer Engineering, University of Camp ...;Engineering, Modeling and Applied Social Sciences Center, UFABC, Brazil;Department of Computer Engineering and Industrial Automation, University of Campinas, Campinas, SP, Brazil and Lab. of Signal Processing for Communications, School of Electrical and Computer Engin ...
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 work, we present a discussion concerning some fundamental aspects of sparse component analysis (SCA), a methodology that has been increasingly employed to solve some challenging signal processing problems. In particular, we present some insights into the use of l1 norm as a quantifier of sparseness and its application as a cost function to solve the blind source separation (BSS) problem. We also provide results on experiments in which source extraction was successfully made when we performed a search for sparse components in the mixtures of sparse signals. Finally, we make an analysis of the behavior of this approach on scenarios in which the source signals are not sparse.