Semi-Supervised Multi-View Correlation Feature Learning with Application to Webpage Classification

Abstract

Webpage classification has attracted a lot of research interest. Webpage data is often multi-view and high-dimensional, and the webpage classification application is usually semi-supervised. Due to these characteristics, using semi-supervised multi-view feature learning (SMFL) technique to deal with the webpage classification problem has recently received much attention. However, there still exists room for improvement for this kind of feature learning technique. How to effectively utilize the correlation information among multi-view of webpage data is an important research topic. Correlation analysis on multi-view data can facilitate extraction of the complementary information. In this paper, we propose a novel SMFL approach, named semi-supervised multi-view correlation feature learning (SMCFL), for webpage classification. SMCFL seeks for a discriminant common space by learning a multi-view shared transformation in a semi-supervised manner. In the discriminant space, the correlation between intra-class samples is maximized, and the correlation between inter-class samples and the global correlation among both labeled and unlabeled samples are minimized simultaneously. We transform the matrix-variable based nonconvex objective function of SMCFL into a convex quadratic programming problem with one real variable, and can achieve a global optimal solution. Experiments on widely used datasets demonstrate the effectiveness and efficiency of the proposed approach.