Sparse Proximal Support Vector Machines for feature selection in high dimensional datasets
Sparse Proximal Support Vector Machines is an embedded feature selection method. sPSVMs removes more than 98% of features in many high dimensional datasets. An efficient alternating optimization technique is proposed. sPSVMs induces class-specific local sparsity.
high dimensional datasets embedded feature selection sparsity regularization class-specific feature selection