  In this paper, we propose a novel sparse learning based feature selection
method that directly optimizes a large margin linear classification model
sparsity with l_(2,p)-norm (0 < p < 1)subject to data-fitting constraints,
rather than using the sparsity as a regularization term. To solve the direct
sparsity optimization problem that is non-smooth and non-convex when 0<p<1, we
provide an efficient iterative algorithm with proved convergence by converting
it to a convex and smooth optimization problem at every iteration step. The
proposed algorithm has been evaluated based on publicly available datasets, and
extensive comparison experiments have demonstrated that our algorithm could
achieve feature selection performance competitive to state-of-the-art
algorithms.
