Sparse Support Vector Machine with Fisher-Regularizer for Data Reduction

The goal of data reduction is to remove noise features or samples from the original data and hence improve the performance of algorithms. Although Fisher-regularized support vector machine (FisherSVM) has good separability, FisherSVM lacks sparsity to reduce noise data. To alleviate this issue, we propose a novel sparse support vector machine with Fisher-regularizer (SF-SVM). The
$L_{1}$
-norm of model coefficients and the Fisher-regularizer term used in SF-SVM can induce a sparse model and improve the generalization performance, respectively. Besides, the proposed SF-SVM includes two versions: linear SF-SVM and kernel SF-SVM, which can perform feature and sample reduction with favorable sparsity, respectively. Experimental results on seven UCI datasets show that the proposed SF-SVM has a satisfactory data reduction ability and good classification performance.