Blind Source Separation of Overdetermined Linear-Quadratic Mixtures View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2010

AUTHORS

David Hutchison , Takeo Kanade , Josef Kittler , Jon M. Kleinberg , Friedemann Mattern , John C. Mitchell , Moni Naor , Oscar Nierstrasz , C. Pandu Rangan , Bernhard Steffen , Madhu Sudan , Demetri Terzopoulos , Doug Tygar , Moshe Y. Vardi , Gerhard Weikum , Leonardo T. Duarte , Ricardo Suyama , Romis Attux , Yannick Deville , João M. T. Romano , Christian Jutten

ABSTRACT

This work deals with the problem of source separation in overdetermined linear-quadratic (LQ) models. Although the mixing model in this situation can be inverted by linear structures, we show that some simple independent component analysis (ICA) strategies that are often employed in the linear case cannot be used with the studied model. Motivated by this fact, we consider the more complex yet more robust ICA framework based on the minimization of the mutual information. Special attention is given to the development of a solution that be as robust as possible to suboptimal convergences. This is achieved by defining a method composed of a global optimization step followed by a local search procedure. Simulations confirm the effectiveness of the proposal. More... »

PAGES

263-270

References to SciGraph publications

  • 2003-06-18. Blind separation of linear-quadratic mixtures of real sources using a recurrent structure in ARTIFICIAL NEURAL NETS PROBLEM SOLVING METHODS
  • Book

    TITLE

    Latent Variable Analysis and Signal Separation

    ISBN

    978-3-642-15994-7
    978-3-642-15995-4

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-642-15995-4_33

    DOI

    http://dx.doi.org/10.1007/978-3-642-15995-4_33

    DIMENSIONS

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