Application of Non-Sparse Manifold Regularized Multiple Kernel Classifier

Non-sparse multiple kernel learning is efficient but not directly able to be applied in a semi-supervised scenario; therefore, we extend it to semi-supervised learning by using a manifold regularization. The manifold regularization is based on a graph constructed on all the data samples including the labeled and the unlabeled, and forces the regularized classifier smooth along the graph. In this study, we propose the manifold regularized p-norm multiple kernels model and provide the solutions with proofs. The risk bound is briefly introduced based on the local Rademacher complexity. Experiments on several datasets and comparisons with several methods show that the efficiency of the proposed model to be used in semi-supervised scenario.