Abstract: To deal with the high-dimensional sparse modeling problem, this paper derives a zero-norm penalized feature selection criterion based on the the empirical risk minimization principle, and combines it with support vector machines through an embedded paradigm. Numerical results on both synthetic and real data sets show that the proposed approach does not only perform well for feature selection tasks, but also shows good performance compared to the conventional sparse modeling techniques, in the sense of classification accuracy and generalization capability.