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We consider the problem of feature extraction for kernel machines. One of the key challenges in this problem is how to detect discriminative features while mapping features into kernel spaces. In this paper, we propose a novel strategy to quantify the importance of features. Firstly, we derive an informative energy model to quantification of feature difference. Secondly, we move the features in the same class closer and push away those belong to different classes according to the model and derivate its objective function. Finally, gradient learning is employed to maximize this function. Experimental results on real data sets have shown the efficient and effective in dealing with projection and classification.

In this paper, information energy metric (IEM) is obtained by similarity computing for high-dimensional samples in a reproducing kernel Hilbert space (RKHS). Firstly, similar/dissimilar subsets and their corresponding informative energy functions are defined. Secondly, IEM is proposed for similarity measure of those subsets, which converts the non-metric distances into metric ones. Finally, applications of this metric is introduced, such as classification problems. Experimental results validate the effectiveness of the proposed method.