Document Type

Conference Paper

Department/Unit

Department of Computer Science

Title

Kernel learning for local learning based clustering

Language

English

Abstract

For most kernel-based clustering algorithms, their performance will heavily hinge on the choice of kernel. In this paper, we propose a novel kernel learning algorithm within the framework of the Local Learning based Clustering (LLC) (Wu and Schölkopf 2006). Given multiple kernels, we associate a non-negative weight with each Hilbert space for the corresponding kernel, and then extend our previous work on feature selection (Zeng and Cheung 2009) to select the suitable Hilbert spaces for LLC. We show that it naturally renders a linear combination of kernels. Accordingly, the kernel weights are estimated iteratively with the local learning based clustering. The experimental results demonstrate the effectiveness of the proposed algorithm on the benchmark document datasets. © 2009 Springer Berlin Heidelberg.

Publication Date

2009

Source Publication Title

Artificial Neural Networks – ICANN 2009: 19th International Conference, Limassol, Cyprus, September 14-17, 2009, Proceedings, Part I

Start Page

10

End Page

19

Conference Location

Limassol, Cyprus

Publisher

Springer

DOI

10.1007/978-3-642-04274-4_2

Link to Publisher's Edition

http://dx.doi.org/10.1007/978-3-642-04274-4_2

ISBN (print)

9783642042737

ISBN (electronic)

9783642042744

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