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Erschienen in:
2010 | OriginalPaper | Buchkapitel
Blind Extraction of the Sparsest Component
verfasst von : Everton Z. Nadalin, André K. Takahata, Leonardo T. Duarte, Ricardo Suyama, Romis Attux
Erschienen in: Latent Variable Analysis and Signal Separation
Verlag: Springer Berlin Heidelberg
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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 ℓ
1
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.