Kernel learning for local learning based clustering

Hong Zeng; Yiu Ming CHEUNG

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

Kernel learning for local learning based clustering

Hong Zeng^*, Yiu Ming CHEUNG

^*Corresponding author for this work

Department of Computer Science

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

5 Citations (Scopus)

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.

Original language	English
Title of host publication	Artificial Neural Networks - ICANN 2009 - 19th International Conference, Proceedings
Pages	10-19
Number of pages	10
Edition	PART 1
DOIs	https://doi.org/10.1007/978-3-642-04274-4_2
Publication status	Published - 2009
Event	19th International Conference on Artificial Neural Networks, ICANN 2009 - Limassol, Cyprus Duration: 14 Sept 2009 → 17 Sept 2009

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 1
Volume	5768 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	19th International Conference on Artificial Neural Networks, ICANN 2009
Country/Territory	Cyprus
City	Limassol
Period	14/09/09 → 17/09/09

Scopus Subject Areas

Theoretical Computer Science
Computer Science(all)

Access to Document

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

Cite this

Zeng, H., & CHEUNG, Y. M. (2009). Kernel learning for local learning based clustering. In Artificial Neural Networks - ICANN 2009 - 19th International Conference, Proceedings (PART 1 ed., pp. 10-19). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5768 LNCS, No. PART 1). https://doi.org/10.1007/978-3-642-04274-4_2

@inproceedings{60380205777c4117891649c32543d705,

title = "Kernel learning for local learning based clustering",

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 Sch{\"o}lkopf 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.",

author = "Hong Zeng and CHEUNG, {Yiu Ming}",

year = "2009",

doi = "10.1007/978-3-642-04274-4_2",

language = "English",

isbn = "3642042732",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

number = "PART 1",

pages = "10--19",

booktitle = "Artificial Neural Networks - ICANN 2009 - 19th International Conference, Proceedings",

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}

Zeng, H & CHEUNG, YM 2009, Kernel learning for local learning based clustering. in Artificial Neural Networks - ICANN 2009 - 19th International Conference, Proceedings. PART 1 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 5768 LNCS, pp. 10-19, 19th International Conference on Artificial Neural Networks, ICANN 2009, Limassol, Cyprus, 14/09/09. https://doi.org/10.1007/978-3-642-04274-4_2

Kernel learning for local learning based clustering. / Zeng, Hong; CHEUNG, Yiu Ming.
Artificial Neural Networks - ICANN 2009 - 19th International Conference, Proceedings. PART 1. ed. 2009. p. 10-19 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5768 LNCS, No. PART 1).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

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AU - CHEUNG, Yiu Ming

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N2 - 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.

AB - 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.

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U2 - 10.1007/978-3-642-04274-4_2

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

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SN - 9783642042737

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Artificial Neural Networks - ICANN 2009 - 19th International Conference, Proceedings

T2 - 19th International Conference on Artificial Neural Networks, ICANN 2009

Y2 - 14 September 2009 through 17 September 2009

ER -

Kernel learning for local learning based clustering

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