BBAuthors: Xu, Jinglin; Advisor: -; Participants: Han, Junwei; Nie, Feiping; Li, Xuelong (2020)
With the explosive growth of data, the multi-view data is widely used in many fields, such as data mining, machine learning, computer vision and so on. Because such data always has a complex structure, i.e. many categories, many perspectives of description and high dimension, how to formulate an accurate and reliable framework for the multi-view classification is a very challenging task. In this paper, we propose a novel multi-view classification method by using multiple multi-class Support Vector Machines (SVMs) with a novel collaborative strategy. Here each multi-class SVM embeds the scaling factor to renewedly adjust the weight allocation of all features, which is beneficial to hig... 
