Source separation and density estimation by faithful equivariant SOM

Juan K. Lin, David G. Grier, Jack D. Cowan

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    We couple the tasks of source separation and density estimation by extracting the local geometrical structure of distributions obtained from mixtures of statistically independent sources. Our modifications of the self-organizing map (SOM) algorithm results in purely digital learning rules which perform non-parametric histogram density estimation. The non-parametric nature of the separation allows for source separation of non-linear mixtures. An anisotropic coupling is introduced into our SOM with the role of aligning the network locally with the independent component contours. This approach provides an exact verification condition for source separation with no prior on the source distributions.

    Original languageEnglish (US)
    Title of host publicationAdvances in Neural Information Processing Systems 9 - Proceedings of the 1996 Conference, NIPS 1996
    PublisherNeural information processing systems foundation
    Pages536-542
    Number of pages7
    ISBN (Print)0262100657, 9780262100656
    StatePublished - 1997
    Event10th Annual Conference on Neural Information Processing Systems, NIPS 1996 - Denver, CO, United States
    Duration: Dec 2 1996Dec 5 1996

    Publication series

    NameAdvances in Neural Information Processing Systems
    ISSN (Print)1049-5258

    Other

    Other10th Annual Conference on Neural Information Processing Systems, NIPS 1996
    CountryUnited States
    CityDenver, CO
    Period12/2/9612/5/96

    ASJC Scopus subject areas

    • Computer Networks and Communications
    • Information Systems
    • Signal Processing

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