Clustering via Kernel Decomposition

Anna Szynkowiak Have, Mark A. Girolami, Jan Larsen

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    Abstract

    Methods for spectral clustering have been proposed recently which rely on the eigenvalue decomposition of an affinity matrix. In this work it is proposed that the affinity matrix is created based on the elements of a non-parametric density estimator. This matrix is then decomposed to obtain posterior probabilities of class membership using an appropriate form of nonnegative matrix factorization. The troublesome selection of hyperparameters such as kernel width and number of clusters can be obtained using standard cross-validation methods as is demonstrated on a number of diverse data sets.
    Original languageEnglish
    JournalI E E E Transactions on Neural Networks
    Volume17
    Issue number1
    Pages (from-to)256-264
    ISSN1045-9227
    DOIs
    Publication statusPublished - 2006

    Bibliographical note

    Copyright: 2006 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE

    Keywords

    • probabilistic clustering
    • aggregated Markov models
    • spectral clustering
    • kernel principal component analysis
    • kernel decomposition

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