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Blind Extraction of the Sparsest Component

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Latent Variable Analysis and Signal Separation (LVA/ICA 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6365))

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.

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Author information

Authors and Affiliations

  1. Department of Computer Engineering and Industrial Automation, University of Campinas – UNICAMP, CEP 13083-970, Campinas, SP, Brazil

    Everton Z. Nadalin & Romis Attux

  2. Department of Microwave and Optics, University of Campinas – UNICAMP, CEP 13083-970, Campinas, SP, Brazil

    Leonardo T. Duarte

  3. Department of Communication, University of Campinas – UNICAMP, CEP 13083-970, Campinas, SP, Brazil

    André K. Takahata

  4. Lab. of Signal Processing for Communications (DSPCom), School of Electrical and Computer Engineering, University of Campinas – UNICAMP, P.O. Box 6101, CEP 13083-970, Campinas, SP, Brazil

    Everton Z. Nadalin, André K. Takahata, Leonardo T. Duarte & Romis Attux

  5. Engineering, Modeling and Applied Social Sciences Center (CECS), UFABC, Brazil

    Ricardo Suyama

Authors
  1. Everton Z. Nadalin
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  2. André K. Takahata
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  3. Leonardo T. Duarte
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  4. Ricardo Suyama
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  5. Romis Attux
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Editor information

Editors and Affiliations

  1. Dept. of Electrical Engineering, Universitè d’Evry Val d’Essone, 40 rue du Pelvoux, 91020, Courcouronnes, France

    Vincent Vigneron

  2. Laboratoire I3S, Les Algorithmes - Euclide-B, BP 121, Université de Nice-Sophia Antipolis, 2000 Route des Lucioles, 06903, Sophia Antipolis Cedex, France

    Vicente Zarzoso

  3. School of Engineering, Dept. of Telecommunications, ISITSchool of Engineering, Dept. of Telecommunications, ISITV, Université de Toulon, Avenue George Pompidou, BP 56, La Valette du Var, Cedex, 83162, France

    Eric Moreau

  4. INRIA France, Equipe-projet METISS, Centre de Recherche INRIA Rennes-Bretagne Atlantique, Campus de Beaulieu, 35042, Rennes cedex, France

    Rémi Gribonval

  5. INRIA France, Equipe-projet METISS, Centre de Recherche INRIA Rennes-Bretagne Atlantique, Campus de Beaulieu, 35042, Rennes Cedex, France

    Emmanuel Vincent

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© 2010 Springer-Verlag Berlin Heidelberg

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Nadalin, E.Z., Takahata, A.K., Duarte, L.T., Suyama, R., Attux, R. (2010). Blind Extraction of the Sparsest Component. In: Vigneron, V., Zarzoso, V., Moreau, E., Gribonval, R., Vincent, E. (eds) Latent Variable Analysis and Signal Separation. LVA/ICA 2010. Lecture Notes in Computer Science, vol 6365. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15995-4_49

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  • DOI: https://doi.org/10.1007/978-3-642-15995-4_49

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15994-7

  • Online ISBN: 978-3-642-15995-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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