An approach of moment-based algorithm for noisy ICA models

Daisuke Ito, Noboru Murata

    Research output: Contribution to journalArticle

    Abstract

    Factor analysis is well known technique to uncorrelate observed signals with Gaussina noises before ICA (Independent Component Analysis) algorithms are applied. However, factor analysis is not applicable when the number of source signals are more than that of Ledermann's bound, and when the observations are contaminated by non-Gaussian noises. In this paper, an approach is proposed based on higher-order moments of signals and noises in order to overcome those constraints.

    Original languageEnglish
    Pages (from-to)343-349
    Number of pages7
    JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume3195
    Publication statusPublished - 2004

    Fingerprint

    Independent component analysis
    Independent Component Analysis
    Factor analysis
    Statistical Factor Analysis
    Factor Analysis
    Moment
    Non-Gaussian Noise
    Noise
    Higher Order Moments
    Model

    ASJC Scopus subject areas

    • Computer Science(all)
    • Biochemistry, Genetics and Molecular Biology(all)
    • Theoretical Computer Science

    Cite this

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    abstract = "Factor analysis is well known technique to uncorrelate observed signals with Gaussina noises before ICA (Independent Component Analysis) algorithms are applied. However, factor analysis is not applicable when the number of source signals are more than that of Ledermann's bound, and when the observations are contaminated by non-Gaussian noises. In this paper, an approach is proposed based on higher-order moments of signals and noises in order to overcome those constraints.",
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