Application of Kernel Learning Vector Quantization to Novelty Detection

被引：0

作者：

Xing, Hongjie ^{[1
]}

Wang, Xizhao ^{[1
]}

Zhu, Ruixian ^{[1
]}

Wang, Dan ^{[1
]}

机构：

[1] Hebei Univ, Coll Math & Comp Sci, Baoding, Hebei Province, Peoples R China

来源：

2008 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS (SMC), VOLS 1-6 | 2008年

关键词：

Kernel learning vector quantization; Novelty detection; Kernel self-organizing map;

D O I：

10.1109/ICSMC.2008.4811315

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：439 / 443

页数：5

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