A Rival Penalized EM Algorithm towards Maximizing Weighted Likelihood for Density Mixture Clustering with Automatic Model Selection

Authors:
Yiu-ming Cheung
Affiliations:
Hong Kong Baptist University, Hong Kong, China
Venue:
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Year:
2004

Citing 0
Cited 3

On Weight Design of Maximum Weighted Likelihood and an Extended EM Algorithm

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Feature Weighted Rival Penalized EM for Gaussian Mixture Clustering: Automatic Feature and Model Selections in a Single Paradigm

Computational Intelligence and Security
A new feature selection method for Gaussian mixture clustering

Pattern Recognition

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Abstract

How to determine the number of clusters is an intractable problem in clustering analysis. In this paper, we propose a new learning paradigm named Maximum Weighted Likelihood (MwL), in which the weights are designable. Accordingly, we develop a novel Rival Penalized Expectation-Maximization (RPEM) algorithm, whose intrinsic rival penalization mechanism enables the redundant densities in the mixture to be gradually faded out during the learning. Hence, the RPEM can automatically select an appropriate number of densities in density mixture clustering. The experiments have shown the promising results.