Kernel methods for heterogeneous feature selection

This paper introduces two feature selection methods to deal with heteroge-neous data that include continuous and categorical variables. We propose to plug a dedicated kernel that handles both kinds of variables into a Recursive Feature Elimination procedure using either a non-linear SVM or Multiple Kernel Learning. These methods are shown to offer state-of-the-art perfor-mances on a variety of high-dimensional classification tasks