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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...
Maximum entropy discrimination (MED) is a general framework for discriminative estimation based on the maximum entropy and large margin principles, but it just uses only one view of the data not all the views of the data. Although Multi-view maximum entropy discrimination considered the multi-view information of the data, it just respects the consensus principle. There are two common principles named...
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