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ICANN
2010
Springer

A Cooperative and Penalized Competitive Learning Approach to Gaussian Mixture Clustering

9 years 2 months ago
A Cooperative and Penalized Competitive Learning Approach to Gaussian Mixture Clustering
Abstract. Competitive learning approaches with penalization or cooperation mechanism have been applied to unsupervised data clustering due to their attractive ability of automatic cluster number selection. In this paper, we further investigate the properties of different competitive strategies and propose a novel learning algorithm called Cooperative and Penalized Competitive Learning (CPCL), which implements the cooperation and penalization mechanisms simultaneously in a single competitive learning process. The integration of these two different kinds of competition mechanisms enables the CPCL to have good convergence speed, precision and robustness. Experiments on Gaussian mixture clustering are performed to investigate the proposed algorithm. The promising results demonstrate its superiority.
Yiu-ming Cheung, Hong Jia
Added 07 Dec 2010
Updated 07 Dec 2010
Type Conference
Year 2010
Where ICANN
Authors Yiu-ming Cheung, Hong Jia
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