Kernel MDL to determine the number of clusters

被引：0

作者：

Kyrgyzov, Ivan O. ^{[1
]}

Kyrgyzov, Olexiy O. ^{[2
]}

Maitre, Henri ^{[1
]}

Campedel, Marine ^{[1
]}

机构：

[1] Telecom Paris LTCI, GET, Competence Ctr Informat Extract, CNRS,UMR 5141, 40 Rue Barrault, F-75013 Paris, France

[2] Oregon Hlth & Sci Univ, OGI Sch Sci & Engn, Dept Comp Sci & Elect Engn, Beaverton, OR 97006 USA

来源：

MACHINE LEARNING AND DATA MINING IN PATTERN RECOGNITION, PROCEEDINGS | 2007年 / 4571卷

关键词：

D O I：

暂无

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 propose a new criterion, based on Minimum Description Length (MDL), to estimate an optimal number of clusters. This criterion, called Kernel MDL (KMDL), is particularly adapted to the use of kernel K-means clustering algorithm. Its formulation is based on the definition of MDL derived for Gaussian Mixture Model (GMM). We demonstrate the efficiency of our approach on both synthetic data and real data such as SPOT5 satellite images.

引用

页码：203 / +

页数：3

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