Algorithms for clustering data
Algorithms for clustering data
Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
The minimum description length principle in coding and modeling
IEEE Transactions on Information Theory
A novel method for image retrieval using relevance feedback and unsupervised clustering
COMPUTE '11 Proceedings of the Fourth Annual ACM Bangalore Conference
Cluster validity measures based on the minimum description length principle
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part I
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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.