Sharing features in multi-class boosting via group sparsity

We present a novel formulation of fully corrective boosting for multi-class classification problems with the awareness of sharing features. Our multi-class boosting is solved in a single optimization problem. In order to share features across different classes, we introduce the mixed-norm regularization, which promotes group sparsity, into boosting. We then derive the Lagrange dual problems which enable us to design fully corrective multi-class algorithms using the primal-dual optimization technique. We show that sharing features across classes can improve classification performance and efficiency. We empirically show that in many cases, the proposed multi-class boosting generalizes better than a range of competing multi-class boosting algorithms due to the capability of feature sharing. Experimental results on machine learning data, visual scene and object recognition demonstrate the efficiency and effectiveness of proposed algorithms and validate our theoretical findings.