A New Approach for High-Dimensional Unsupervised Learning: Applications to Image Restoration
This paper proposes an unsupervised algorithm for learning a high-dimensional finite generalized Dirichlet mixture model. The generalized Dirichlet distribution offers high flexibility and ease of use. We propose a hybrid stochastic expectation maximization algorithm (HSEM) to estimate the parameters of the generalized Dirichlet mixture. The performance of our method is tested by applying it to the problems of image restoration.
KeywordsImage Restoration Dirichlet Distribution Finite Mixture Model Local Quadratic Convergence Generalize Dirichlet Distribution
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