Application of kernel learning vector quantization to novelty detection

Hongjie XING; Xizhao WANG; Ruixian ZHU; Dan WANG

doi:10.1109/ICSMC.2008.4811315

Application of kernel learning vector quantization to novelty detection

Hongjie XING, Xizhao WANG, Ruixian ZHU, Dan WANG

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Referred Conference Paper › peer-review

1 Citation (Scopus)

Abstract

In this paper, we focus on kernel learning vector quantization (KLVQ) for handling novelty detection. The two key issues are addressed: the existing KLVQ methods are reviewed and revisited, while the reformulated KLVQ is applied to tackle novelty detection problems. Although the calculation of kernelising the learning vector quantization (LVQ) may add an extra computational cost, the proposed method exhibits better performance over the LVQ. The numerical study on one synthetic data set confirms the benefit in using the proposed KLVQ.

Original language	English
Title of host publication	Proceedings : 2008 IEEE International Conference on Systems, Man and Cybernetics, SMC 2008
Publisher	IEEE
Pages	439-443
Number of pages	5
ISBN (Print)	9781424423835
DOIs	https://doi.org/10.1109/ICSMC.2008.4811315
Publication status	Published - 2008
Externally published	Yes
Event	2008 IEEE International Conference on Systems, Man and Cybernetics, SMC 2008 - Singapore, Singapore Duration: 12 Oct 2008 → 15 Oct 2008

Publication series

Name	IEEE International Conference on Systems, Man and Cybernetics
Publisher	IEEE
ISSN (Print)	1062-922X

Conference

Conference	2008 IEEE International Conference on Systems, Man and Cybernetics, SMC 2008
Country/Territory	Singapore
City	Singapore
Period	12/10/08 → 15/10/08

Bibliographical note

This work is supported by the natural science foundation of Hebei Province (F2008000635), by the key project foundation of applied fundamental research of Hebei Province (08963522D), by the plan of 100 excellent innovative scientists of the first group in Education Department of Hebei Province, and by the scientific research foundation of Hebei Province (06213548).

Keywords

Kernel learning vector quantization
Kernel self-organizing map
Novelty detection

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10.1109/ICSMC.2008.4811315

Cite this

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title = "Application of kernel learning vector quantization to novelty detection",

abstract = "In this paper, we focus on kernel learning vector quantization (KLVQ) for handling novelty detection. The two key issues are addressed: the existing KLVQ methods are reviewed and revisited, while the reformulated KLVQ is applied to tackle novelty detection problems. Although the calculation of kernelising the learning vector quantization (LVQ) may add an extra computational cost, the proposed method exhibits better performance over the LVQ. The numerical study on one synthetic data set confirms the benefit in using the proposed KLVQ.",

keywords = "Kernel learning vector quantization, Kernel self-organizing map, Novelty detection",

author = "Hongjie XING and Xizhao WANG and Ruixian ZHU and Dan WANG",

note = "This work is supported by the natural science foundation of Hebei Province (F2008000635), by the key project foundation of applied fundamental research of Hebei Province (08963522D), by the plan of 100 excellent innovative scientists of the first group in Education Department of Hebei Province, and by the scientific research foundation of Hebei Province (06213548).; 2008 IEEE International Conference on Systems, Man and Cybernetics, SMC 2008 ; Conference date: 12-10-2008 Through 15-10-2008",

year = "2008",

doi = "10.1109/ICSMC.2008.4811315",

language = "English",

isbn = "9781424423835",

series = "IEEE International Conference on Systems, Man and Cybernetics",

publisher = "IEEE",

pages = "439--443",

booktitle = "Proceedings : 2008 IEEE International Conference on Systems, Man and Cybernetics, SMC 2008",

}

XING, H, WANG, X, ZHU, R & WANG, D 2008, Application of kernel learning vector quantization to novelty detection. in Proceedings : 2008 IEEE International Conference on Systems, Man and Cybernetics, SMC 2008. IEEE International Conference on Systems, Man and Cybernetics, IEEE, pp. 439-443, 2008 IEEE International Conference on Systems, Man and Cybernetics, SMC 2008, Singapore, Singapore, 12/10/08. https://doi.org/10.1109/ICSMC.2008.4811315

Application of kernel learning vector quantization to novelty detection. / XING, Hongjie; WANG, Xizhao; ZHU, Ruixian et al.
Proceedings : 2008 IEEE International Conference on Systems, Man and Cybernetics, SMC 2008. IEEE, 2008. p. 439-443 (IEEE International Conference on Systems, Man and Cybernetics).

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Referred Conference Paper › peer-review

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T1 - Application of kernel learning vector quantization to novelty detection

AU - XING, Hongjie

AU - WANG, Xizhao

AU - ZHU, Ruixian

AU - WANG, Dan

N1 - This work is supported by the natural science foundation of Hebei Province (F2008000635), by the key project foundation of applied fundamental research of Hebei Province (08963522D), by the plan of 100 excellent innovative scientists of the first group in Education Department of Hebei Province, and by the scientific research foundation of Hebei Province (06213548).

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N2 - In this paper, we focus on kernel learning vector quantization (KLVQ) for handling novelty detection. The two key issues are addressed: the existing KLVQ methods are reviewed and revisited, while the reformulated KLVQ is applied to tackle novelty detection problems. Although the calculation of kernelising the learning vector quantization (LVQ) may add an extra computational cost, the proposed method exhibits better performance over the LVQ. The numerical study on one synthetic data set confirms the benefit in using the proposed KLVQ.

AB - In this paper, we focus on kernel learning vector quantization (KLVQ) for handling novelty detection. The two key issues are addressed: the existing KLVQ methods are reviewed and revisited, while the reformulated KLVQ is applied to tackle novelty detection problems. Although the calculation of kernelising the learning vector quantization (LVQ) may add an extra computational cost, the proposed method exhibits better performance over the LVQ. The numerical study on one synthetic data set confirms the benefit in using the proposed KLVQ.

KW - Kernel learning vector quantization

KW - Kernel self-organizing map

KW - Novelty detection

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M3 - Conference paper (refereed)

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SN - 9781424423835

T3 - IEEE International Conference on Systems, Man and Cybernetics

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BT - Proceedings : 2008 IEEE International Conference on Systems, Man and Cybernetics, SMC 2008

PB - IEEE

T2 - 2008 IEEE International Conference on Systems, Man and Cybernetics, SMC 2008

Y2 - 12 October 2008 through 15 October 2008

ER -

Application of kernel learning vector quantization to novelty detection

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Keywords

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