Simplifying mixture Models through Function Approximation Simplifying Mixture Models through Function Approximation

The finite mixture model is widely used in various statistical learning problems. However, the model obtained may contain a large number of components, making it inefficient in practical applications. In this paper, we propose to simplify the mixture model by first grouping similar components together and then performing local fitting through function approximation. By using the squared loss to measure the distance between mixture models, our algorithm naturally combines the two different tasks of component clustering and model simplification. The proposed method can be used to speed up various algorithms that use mixture models during training (e.g., Bayesian filtering, belief propagation) or testing (e.g., kernel density estimation, SVM testing). Encouraging results are observed in the experiments on density estimation, clustering-based image segmentation and simplification of SVM decision functions.