Robust Transductive Support Vector Machine for Multi-View Classification

Abstract

Semi-Supervised Learning (SSL) aims to improve the performance of models trained with a small set of labeled data and a large collection of unlabeled data. Learning multi-view representations from different perspectives of data has proved to be very effectively for improving generalization performance. However, existing semi-supervised multi-view learning methods tend to ignore the specific difficulty of different unlabeled examples, such as the outliers and noise, leading to error-prone classification. To address this problem, this paper proposes Robust Transductive Support Vector Machine (RTSVM) that introduces the margin distribution into TSVM, which is robust to the outliers and noise. Specifically, the first-order (margin mean) and second-order statistics (margin variance) are regularized into TSVM, which try to achieve strong generalization performance. Then, we impose a global similarity constraint between distinct RTSVMs each trained from one view of the data. Moreover, our algorithm runs with fast convergence by using concave–convex procedure. Finally, we validate our proposed method on a variety of multi-view datasets, and the experimental results demonstrate that our proposed algorithm is effective. By exploring large amount of unlabeled examples and being robust to the outliers and noise among different views, the generalization performance of our method show the superiority to single-view learning and other semi-supervised multi-view learning methods.