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2003
Springer

Kernel Trick Embedded Gaussian Mixture Model

12 years 2 months ago
Kernel Trick Embedded Gaussian Mixture Model
In this paper, we present a kernel trick embedded Gaussian Mixture Model (GMM), called kernel GMM. The basic idea is to embed kernel trick into EM algorithm and deduce a parameter estimation algorithm for GMM in feature space. Kernel GMM could be viewed as a Bayesian Kernel Method. Compared with most classical kernel methods, the proposed method can solve problems in probabilistic framework. Moreover, it can tackle nonlinear problems better than the traditional GMM. To avoid great computational cost problem existing in most kernel methods upon large scale data set, we also employ a Monte Carlo sampling technique to speed up kernel GMM so that it is more practical and efficient. Experimental results on synthetic and real-world data set demonstrate that the proposed approach has satisfing performance.
Jingdong Wang, Jianguo Lee, Changshui Zhang
Added 15 Mar 2010
Updated 15 Mar 2010
Type Conference
Year 2003
Where ALT
Authors Jingdong Wang, Jianguo Lee, Changshui Zhang
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