Sparse support vector machines by kernel discriminant analysis
نویسندگان
چکیده
We discuss sparse support vector machines (SVMs) by selecting the linearly independent data in the empirical feature space. First we select training data that maximally separate two classes in the empirical feature space. As a selection criterion we use linear discriminant analysis in the empirical feature space and select training data by forward selection. Then the SVM is trained in the empirical feature space spanned by the selected training data. We evaluate our method by computer experiments and show that our method can realize sparse SVMs with comparable generalization performance with that of regular SVMs.
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