Multi-view intuitionistic fuzzy support vector machines with insensitive pinball loss for classification of noisy data

Abstract

Multi-view support vector machines (MvSVMs) have been widely used to solve multi-view classification problems. However, the conventional MvSVMs often overlook the presence of noise and outliers that commonly exist in the original data. In this paper, we propose two novel multi-view intuitionistic fuzzy support vector machines with insensitive pinball loss that can not only handle the general multi-view classification problems but also be robust to noisy data. In the proposed convex optimization models, the pinball loss is incorporated into the multi-view learning, which enables the maximization of the quantization distance. Moreover, to utilize multi-view information more effectively, the intuitionistic fuzzy score is introduced to assign a weight to each multi-view sample. The intuitionistic fuzzy score combines the membership and non-membership functions, which provides an efficient mechanism to assign weights to the multi-view samples. Further, we provide a discussion of the proposed models with the state-of-the-art technologies. Experiments are conducted on a series of datasets, and the results show that the proposed convex models outperform several state-of-the-art models. The code is available at 1.