Supervised feature selection algorithm via discriminative ridge regression

Abstract

This paper studies a new feature selection method for data classification that efficiently combines the discriminative capability of features with the ridge regression model. It first sets up the global structure of training data with the linear discriminant analysis that assists in identifying the discriminative features. And then, the ridge regression model is employed to assess the feature representation and the discrimination information, so as to obtain the representative coefficient matrix. The importance of features can be calculated with this representative coefficient matrix. Finally, the new subset of selected features is applied to a linear Support Vector Machine for data classification. To validate the efficiency, sets of experiments are conducted with twenty benchmark datasets. The experimental results show that the proposed approach performs much better than the state-of-the-art feature selection algorithms in terms of the evaluating indicator of classification. And the proposed feature selection algorithm possesses a competitive performance compared with existing feature selection algorithms with regard to the computational cost.