LEARNING SPARSE MIXTURE MODELS FOR DISCRIMINATIVE CLASSIFICATION
Weixiang Liu, Nanning Zheng, Songfeng Zheng · International Journal of Pattern Recognition and Artificial Intelligence · 2006
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.