Discriminative Stochastic Neighbor Embedding Analysis Method

A novel linear supervised feature extraction method named discriminative stochastic neighbor embedding(DSNE) is proposed based on the algorithm of stochastic neighbor embedding that is unsupervised and nonlinear.DSNE selects the joint probability to model the pairwise similarities of input samples with class labels.The linear projection matrix is used to discover the underlying structure of data manifold which is nonlinear.The cost function is constructed to minimize the intraclass Kullback-Leibler divergence as well as maximize the interclass Kullback-Leibler divergences.DSNE is evaluated in artificial synthetic data and face database.Experimental results suggest that the proposed algorithm provides a better visualization effectiveness as well as powerful pattern revealing capability for complex manifold data.