A maximum weighted likelihood approach to simultaneous model selection and feature weighting in gaussian mixture

Yiu Ming CHEUNG; Hong Zeng

doi:10.1007/978-3-540-74690-4_9

A maximum weighted likelihood approach to simultaneous model selection and feature weighting in gaussian mixture

Yiu Ming CHEUNG^*, Hong Zeng

^*Corresponding author for this work

Department of Computer Science

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

3 Citations (Scopus)

Abstract

This paper is to identify the clustering structure and the relevant features automatically and simultaneously in the context of Gaussian mixture model. We perform this task by introducing two sets of weight functions under the recently proposed Maximum Weighted Likelihood (MWL) learning framework. One set is to reward the significance of each component in the mixture, and the other one is to discriminate the relevance of each feature to the cluster structure. The experiments on both the synthetic and real-world data show the efficacy of the proposed algorithm.

Original language	English
Title of host publication	Artificial Neural Networks - ICANN 2007 - 17th International Conference, Proceedings
Publisher	Springer Verlag
Pages	78-87
Number of pages	10
Edition	PART 1
ISBN (Print)	9783540746898
DOIs	https://doi.org/10.1007/978-3-540-74690-4_9
Publication status	Published - 2007
Event	17th International Conference on Artificial Neural Networks, ICANN 2007 - Porto, Portugal Duration: 9 Sept 2007 → 13 Sept 2007

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 1
Volume	4668 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	17th International Conference on Artificial Neural Networks, ICANN 2007
Country/Territory	Portugal
City	Porto
Period	9/09/07 → 13/09/07

Scopus Subject Areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-540-74690-4_9

Cite this

CHEUNG, Y. M., & Zeng, H. (2007). A maximum weighted likelihood approach to simultaneous model selection and feature weighting in gaussian mixture. In Artificial Neural Networks - ICANN 2007 - 17th International Conference, Proceedings (PART 1 ed., pp. 78-87). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4668 LNCS, No. PART 1). Springer Verlag. https://doi.org/10.1007/978-3-540-74690-4_9

CHEUNG, Yiu Ming ; Zeng, Hong. / A maximum weighted likelihood approach to simultaneous model selection and feature weighting in gaussian mixture. Artificial Neural Networks - ICANN 2007 - 17th International Conference, Proceedings. PART 1. ed. Springer Verlag, 2007. pp. 78-87 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).

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title = "A maximum weighted likelihood approach to simultaneous model selection and feature weighting in gaussian mixture",

abstract = "This paper is to identify the clustering structure and the relevant features automatically and simultaneously in the context of Gaussian mixture model. We perform this task by introducing two sets of weight functions under the recently proposed Maximum Weighted Likelihood (MWL) learning framework. One set is to reward the significance of each component in the mixture, and the other one is to discriminate the relevance of each feature to the cluster structure. The experiments on both the synthetic and real-world data show the efficacy of the proposed algorithm.",

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CHEUNG, YM & Zeng, H 2007, A maximum weighted likelihood approach to simultaneous model selection and feature weighting in gaussian mixture. in Artificial Neural Networks - ICANN 2007 - 17th 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. 4668 LNCS, Springer Verlag, pp. 78-87, 17th International Conference on Artificial Neural Networks, ICANN 2007, Porto, Portugal, 9/09/07. https://doi.org/10.1007/978-3-540-74690-4_9

A maximum weighted likelihood approach to simultaneous model selection and feature weighting in gaussian mixture. / CHEUNG, Yiu Ming; Zeng, Hong.
Artificial Neural Networks - ICANN 2007 - 17th International Conference, Proceedings. PART 1. ed. Springer Verlag, 2007. p. 78-87 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4668 LNCS, No. PART 1).

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

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AB - This paper is to identify the clustering structure and the relevant features automatically and simultaneously in the context of Gaussian mixture model. We perform this task by introducing two sets of weight functions under the recently proposed Maximum Weighted Likelihood (MWL) learning framework. One set is to reward the significance of each component in the mixture, and the other one is to discriminate the relevance of each feature to the cluster structure. The experiments on both the synthetic and real-world data show the efficacy of the proposed algorithm.

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CHEUNG YM, Zeng H. A maximum weighted likelihood approach to simultaneous model selection and feature weighting in gaussian mixture. In Artificial Neural Networks - ICANN 2007 - 17th International Conference, Proceedings. PART 1 ed. Springer Verlag. 2007. p. 78-87. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). doi: 10.1007/978-3-540-74690-4_9

A maximum weighted likelihood approach to simultaneous model selection and feature weighting in gaussian mixture

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