Kernel learning for local learning based clustering

Hong Zeng*, Yiu Ming CHEUNG

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

5 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationArtificial Neural Networks - ICANN 2009 - 19th International Conference, Proceedings
Number of pages10
EditionPART 1
Publication statusPublished - 2009
Event19th International Conference on Artificial Neural Networks, ICANN 2009 - Limassol, Cyprus
Duration: 14 Sept 200917 Sept 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume5768 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference19th International Conference on Artificial Neural Networks, ICANN 2009

Scopus Subject Areas

  • Theoretical Computer Science
  • Computer Science(all)


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