Semi-supervised Multi-view Individual and Sharable Feature Learning for Webpage Classification

Semi-supervised multi-view feature learning (SMFL) is a feasible solution for webpage classification. However, how to fully extract the complementarity and correlation information effectively under semi-supervised setting has not been well studied. In this paper, we propose a semi-supervised multi-view individual and sharable feature learning (SMISFL) approach, which jointly learns multiple view-individual transformations and one sharable transformation to explore the view-specific property for each view and the common property across views. We design a semi-supervised multi-view similarity preserving term, which fully utilizes the label information of labeled samples and similarity information of unlabeled samples from both intra-view and inter-view aspects. To promote learning of diversity, we impose a constraint on view-individual transformation to make the learned view-specific features to be statistically uncorrelated. Furthermore, we train a linear classifier, such that view-specific and shared features can be effectively combined for classification. Experiments on widely used webpage datasets demonstrate that SMISFL can significantly outperform state-of-the-art SMFL and webpage classification methods.