Sparse algorithm of robust extreme learning machine with l_0-norm

Abstract

Extreme learning machine(ELM) has shown great potential in machine learning because of its high learning rate and strong generalization performance. In order to improve the robustness and sparsity of ELM, on the basis of robust ELM, l_0-norm is introduced as the regularization of the model to improve the sparsity, and a sparse and robust ELM based on l_0-norm regularization is established. A difference of convex(DC) function is used to approximate the l_0-norm. The optimization result is expressed as a DC programming, which is then solved by DC algorithm. Experiments on artificial and benchmark data sets show that the robust ELM based on l_0-norm can be improved in both sparsity and robustness, especially in sparsity.