Semisupervised Multicategory Classification With Imperfect Model

Hong Chen, Luoqing Li

Abstract: Semisupervised learning has been of growing interest over the past years and many methods have been proposed. While existing semisupervised methods have shown some promising empirical performances, their development has been based largely on heuristics. In this paper, we investigate semisupervised multicategory classification with an imperfect mixture density model. In the proposed model, the training data come from a probability distribution, which can be modeled imperfectly by an identifiable mixture distribution. Furthermore, we propose a semisupervised multicategory classification method and establish its generalization error bounds. The theoretical analysis illustrates that the proposed method can utilize unlabeled data effectively and can achieve fast convergence rate.