Semi-supervised multi-view maximum entropy discrimination with expectation Laplacian regularization

Semi-supervised multi-view learning has attracted considerable attention and achieved great success in the machine learning field. This paper proposes a semi-supervised multi-view maximum entropy discrimination approach (SMVMED) with expectation Laplacian regularization for data classification. It takes advantage of the geometric information of the marginal distribution embedded in unlabeled data to construct a semi-supervised classifier. Different from existing methods using Laplacian regularization, we propose to use expectation Laplacian regularization for semi-supervised learning in probabilistic models. We give two implementations of SMVMED and provide their kernel variants. One of them can be relaxed and formulated as a quadratic programming problem that is solved easily. Therefore, for this implementation, we provided two versions which are approximate and exact ones. The experiments on one synthetic and multiple real-world data sets show that SMVMED demonstrates superior performance over semi-supervised single-view maximum entropy discrimination, MVMED and other state-of-the-art semi-supervised multi-view learning methods.