Kernel MDL to Determine the Number of Clusters 1st Edition by Ivan Kyrgyzov, Olexiy Kyrgyzov, Henri Maître, Marine Campedel 9783540734987

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ISBN 13: 9783540734987

Author: Ivan O Kyrgyzov, Olexiy O Kyrgyzov, Henri Maître; Marine Campedel 

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.

Kernel MDL to Determine the Number of Clusters 1st Table of contents:

1 Introduction
2 MDL for the Gaussian Mixture Model

3 Kernel K-means Algorithm

4 Kernel MDL

5 Experiments with synthetic data

6 Experiments with real data: satellite ima

7 Conclusions

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