Learning sparse mixture models for discriminative classification
Recently Saul and Lee proposed a mixture model for discriminative classification of non-negative data via non-negative matrix factorization for feature extraction. In order to improve the generalization, this paper considers a sparse version of the model. The basic idea is to minimize the sum of the weights of un-normalized mixture models for posterior distributions according to regularization method. Experiments on CBCL face database and USPS digit data set assess the validity of the proposed approach.
Discriminative classification, Mixture model, Nonnegative matrix factorization, Regularization method, Sparseness
Liu, Weixiang, Nanning Zheng, and Songfeng Zheng. "Learning sparse mixture models for discriminative classification." International Journal of Pattern Recognition and Artificial Intelligence 20, no. 03 (2006): 431-440.
International Journal of Pattern Recognition and Artificial Intelligence