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Deep Multi-view Multiclass Twin Support Vector Machines by Xijiong Xie; Yanfeng Li; Shiliang Sun is a Computer Science article available to read on EtoBox.

What is Deep Multi-view Multiclass Twin Support Vector Machines about?

Multi-view learning (MVL) is a rapidly evolving direction in the field of machine learning. Despite the positive results, most algorithms that combine multi-view learning with twin support vector machines (TSVM) focus on the traditional machine learning domain. No method has been accomplished for combining MVL, TSVM, and deep learning. In this paper, we propose two novel multi-view deep models to solve the multiclass classification problem, namely deep multi-view twin support vector machines (DMvTSVM) based on deep neural network (DNN) and auto-encoder (AE) network. They find two non-parallel hyperplanes such that each hyperplane is as close to its own class as possible while being as far away from the other class as possible. Meanwhile, we apply similarity regularization to the output of the Deep TSVM classifier for each view to learn consensus information between views, and use this to refine the joint weights of the deep model and TSVM. Finally, the novel models employ the o n e − v s − r e s t strategy to allow the DMvTSVM classifier to solve the multiclass classification problems. In the experiments, the proposed methods are compared with existing state-of-the-art algorithms t

Who reads Deep Multi-view Multiclass Twin Support Vector Machines?

Author

Xijiong Xie; Yanfeng Li; Shiliang Sun

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)