Hypergraph regularized semi-supervised support vector machine

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At present, graph regularized semi-supervised methods achieve excellent performance in various fields. However, the manifold regularization term of most methods only considers the pairwise relationship between data, thus it cannot accurately represent the multivariate and complex structure of data. In this paper, we exploit the multivariate manifold structure by hypergraph, and propose a hypergraph regularized semi-supervised support vector machine (HGSVM) algorithm. To accelerate the training process of HGSVM, we further develop a fast algorithm based boundary sample selection algorithm, termed fast-HGSVM. Moreover, two SMOTE-variant techniques and the one-vs-rest strategy are introduced in fast-HGSVM, and two multi-category semi-supervised algorithms called fast-ASHGSVM and fast-KSHGSVM are proposed. Experiments on two moons and UCI datasets validate the effectiveness of the proposed algorithms.