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An Overview of Blind Source Separation Methods for Linear-Quadratic and Post-nonlinear Mixtures

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Latent Variable Analysis and Signal Separation (LVA/ICA 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9237))

Abstract

Whereas most blind source separation (BSS) and blind mixture identification (BMI) investigations concern linear mixtures (instantaneous or not), various recent works extended BSS and BMI to nonlinear mixing models. They especially focused on two types of models, namely linear-quadratic ones (including their bilinear and quadratic versions, and some polynomial extensions) and post-nonlinear ones. These works are particularly motivated by the associated application fields, which include remote sensing, processing of scanned images (show-through effect) and design of smart chemical and gas sensor arrays. In this paper, we provide an overview of the above two types of mixing models and of the associated BSS and/or BMI methods and applications.

This work was performed in a project jointly supported by CNRS (France) and FAPESP (Brazil). L.T. Duarte thanks CNPq (Brazil) for funding his research.

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Authors and Affiliations

  1. Université de Toulouse, UPS-CNRS-OMP, IRAP (Institut de Recherche en Astrophysique et Planétologie), 14 avenue Edouard Belin, 31400, Toulouse, France

    Yannick Deville

  2. School of Applied Sciences, University of Campinas (UNICAMP), Limeira, Brazil

    Leonardo Tomazeli Duarte

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  1. Yannick Deville
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Correspondence to Yannick Deville .

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  1. Inria, Villers-les-Nancy, France

    Emmanuel Vincent

  2. Tel Aviv University, Tel-Aviv, Israel

    Arie Yeredor

  3. Technical University of Libere, Liberec, Czech Republic

    Zbyněk Koldovský

  4. The Czech Academy of Sciences, Prague, Czech Republic

    Petr Tichavský

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Deville, Y., Duarte, L.T. (2015). An Overview of Blind Source Separation Methods for Linear-Quadratic and Post-nonlinear Mixtures. In: Vincent, E., Yeredor, A., Koldovský, Z., Tichavský, P. (eds) Latent Variable Analysis and Signal Separation. LVA/ICA 2015. Lecture Notes in Computer Science(), vol 9237. Springer, Cham. https://doi.org/10.1007/978-3-319-22482-4_18

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