Semi-supervised classification of multiple kernels embedding manifold information

Abstract

For semi-supervised learning, we propose the Laplacian embedded multiple kernel regression model. As we incorporate the multiple kernel occasion into manifold regularization framework, the models we proposed are flexible in many kinds of datasets and have a solid theoretical foundation. The proposed model can solve the two problems, which are the computation cost of manifold regularization framework and the difficulty in dealing with multi-source or multi-attribute datasets. Though manifold regularization is a convex optimization formulation, it often leads to dense matrix inversion with computation cost. Laplacian embedded method we adopted can solve the problem, however it lacks the proper ability to process complex datasets. Therefore, we further use multiple kernel learning as a part of the proposed model to strengthen its ability. Experiments on several datasets compared with the state-of-the-art methods show the effectiveness and efficiency of the proposed model.