Fast Group Sparse Classification

A recent work [1] proposed a novel group sparse classifier (GSC) that was based on the assumption that the training samples of a particular class approximately form a linear basis for any test sample belonging to that class. The group sparse classifier requires solving an NP hard group-sparsity promoting optimization problem. Thus a convex relaxation of the optimization problem was proposed. The convex optimization problem however, needs to be solved by quadratic programming and hence requires a large amount of computational time. To overcome this, we propose novel greedy (sub-optimal) algorithms for directly solving the NP hard minimization problem. We call the classifiers based on these greedy group sparsity promoting algorithms as fast group sparse classifiers (FGSC).