l0-norm based structural sparse least square regression for feature selection
We impose l0-norm inequality constraint to build the structural sparse LSR problem. We develop an adaptive algorithm to ensure the structural sparsity accurately. Theoretical results on the efficiency and effectiveness of our method are provided. Experimental results prove the superiority of our method over the state-of-the-arts.
l0-norm least square regression feature selection structural sparse learning adaptive greedy algorithm