Kernel Trick Embedded Gaussian Mixture Model

@inproceedings{Wang2003KernelTE,
  title={Kernel Trick Embedded Gaussian Mixture Model},
  author={Jingdong Wang and Jianguo Lee and Changshui Zhang},
  booktitle={ALT},
  year={2003}
}
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… CONTINUE READING
Highly Cited
This paper has 27 citations. REVIEW CITATIONS

Citations

Publications citing this paper.
Showing 1-10 of 17 extracted citations

References

Publications referenced by this paper.
Showing 1-10 of 21 references

Learning with Kernels: support vector machines, regularization, optimization, and beyond

Adaptive computation and machine learning series • 2002
View 7 Excerpts
Highly Influenced

The Nature of Statistical Learning Theory

Statistics for Engineering and Information Science • 2000
View 5 Excerpts
Highly Influenced

The evidence framework applied to support vector machines

IEEE Trans. Neural Netw. Learning Syst. • 2000
View 5 Excerpts
Highly Influenced

Input space versus feature space in kernel-based methods

IEEE Trans. Neural Networks • 1999
View 4 Excerpts
Highly Influenced

Pattern Classification, New York

R. O. Duda, P. E. Hart, D. G. Stork
2001
View 1 Excerpt

Similar Papers

Loading similar papers…