Sparse Norm-Regularized Reconstructive Coefficients Learning
Inspired by the fact that the final decision rule is mainly affected by a small subset of the training samples, i.e., Support Vector Machine(SVM) shows that the decision function relies on the few samples that are on or over the margin. We propose a new framework that explicitly strengthen this intuitive fact by adding an $l_1$-norm regularizer. We give different formulations for our framework in different scenarios, and the experiments show that our framework can not only lead to high sparse solutions but also better performance than traditional methods.