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          Institute: MPI für biologische Kybernetik     Collection: Biologische Kybernetik     Display Documents

ID: 420068.0, MPI für biologische Kybernetik / Biologische Kybernetik
Discriminative K-means for Clustering
Authors:Ye, J.; Zhao, Z.; Wu, M.
Editors:Platt, J. C.; Koller, D.; Singer, Y.; Roweis, S.
Date of Publication (YYYY-MM-DD):2008-09
Title of Proceedings:Advances in Neural Information Processing Systems 20: Proceedings of the 2007 Conference
Start Page:1649
End Page:1656
Physical Description:8
Audience:Not Specified
Intended Educational Use:No
Abstract / Description: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.
External Publication Status:published
Document Type:Conference-Paper
Communicated by:Holger Fischer
Affiliations:MPI f�r biologische Kybernetik/Empirical Inference (Dept. Sch�lkopf)
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