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  Discriminative K-means for Clustering

Ye, J., Zhao, Z., & Wu, M. (2008). Discriminative K-means for Clustering. Advances in Neural Information Processing Systems 20: 21st Annual Conference on Neural Information Processing Systems 2007, 1649-1656.

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Datensatz-Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-C739-B Versions-Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-C73A-9
Genre: Konferenzbeitrag

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 Urheber:
Ye, J, Autor
Zhao, Z, Autor
Wu, M1, Autor           
Platt, Herausgeber
C., J., Herausgeber
Koller, D., Herausgeber
Singer, Y., Herausgeber
Roweis, S., Herausgeber
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

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 Zusammenfassung: We present a theoretical study on the discriminative clustering framework, recently proposed for simultaneous subspace selection via linear discriminant analysis (LDA) and clustering. Empirical results have shown its favorable performance in comparison with several other popular clustering algorithms. However, the inherent relationship between subspace selection and clustering in this framework is not well understood, due to the iterative nature of the algorithm. We show in this paper that this iterative subspace selection and clustering is equivalent to kernel K-means with a specific kernel Gram matrix. This provides significant and new insights into the nature of this subspace selection procedure. Based on this equivalence relationship, we propose the Discriminative K-means (DisKmeans) algorithm for simultaneous LDA subspace selection and clustering, as well as an automatic parameter estimation procedure. We also present the nonlinear extension of DisKmeans using kernels. We show that the learning of the ke rnel matrix over a convex set of pre-specified kernel matrices can be incorporated into the clustering formulation. The connection between DisKmeans and several other clustering algorithms is also analyzed. The presented theories and algorithms are evaluated through experiments on a collection of benchmark data sets.

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 Datum: 2008-09
 Publikationsstatus: Erschienen
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 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Identifikatoren: ISBN: 978-1-605-60352-0
URI: http://nips.cc/Conferences/2007/
BibTex Citekey: 4710
 Art des Abschluß: -

Veranstaltung

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Titel: Twenty-First Annual Conference on Neural Information Processing Systems (NIPS 2007)
Veranstaltungsort: Vancouver, BC, Canada
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Titel: Advances in Neural Information Processing Systems 20: 21st Annual Conference on Neural Information Processing Systems 2007
Genre der Quelle: Zeitschrift
 Urheber:
Affiliations:
Ort, Verlag, Ausgabe: Red Hook, NY, USA : Curran
Seiten: - Band / Heft: - Artikelnummer: - Start- / Endseite: 1649 - 1656 Identifikator: -