A multi-view based semi-supervised classifier with co-regularization for imbalanced data

A method of constructing a multi-view semi-supervised learning classifier was presented for manifold learning and multi-puncture processing. The multi-view and semi-supervised learning of the data is achieved through recursive optimization, and appropriate labeling and equalization processing, until the efficiency of learning becomes stable. The properties of this multi-classifier were given, for instance, an upper bound of the generalization error, which showed a good capacity for generalization. Simulation and empirical analysis showed that the new method performs well with small samples.