Semi-Supervised Learning Based on Semiparametric Regularization

Semi-supervised learning plays an important role in the recent literature on machine learning and data mining and the developed semisupervised learning techniques have led to many data mining applications in recent years. This paper addresses the semi-supervised learning problem by developing a semiparametric regularization based approach, which attempts to discover the marginal distribution of the data to learn the parametric function through exploiting the geometric distribution of the data. This learned parametric function can then be incorporated into the supervised learning on the available labeled data as the prior knowledge. Specifically, our contributions are: (1) We present a semi-supervised learning approach which incorporates the unlabeled data into the supervised learning by a parametric function learned from the whole data including the labeled and unlabeled data. The parametric function reflects the geometric structure of the marginal distribution of the data. Furthermore, the proposed approach which naturally extends to the out-of-sample data is an inductive learning method in nature. (2) This approach allows a family of algorithms to be developed based on various choices of the original RKHS and the loss function. (3) We provide experimental comparisons showing that the proposed approach leads the state-of-the-art performance on a variety of classification tasks. In particular, we demonstrate that this approach can be used successfully in both transductive and semisupervised settings.