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Robust multi-view learning with the bounded LINEX loss by Jingjing Tang; Hao He; Saiji Fu; Yingjie Tian; Gang Kou; Shan Xu is a Computer Science article available to read on EtoBox.

What is Robust multi-view learning with the bounded LINEX loss about?

Multi-view learning, as a promising direction, emphasizes the consensus principle and the complementarity principle to boost the performance. By exploiting view-consistency or view-discrepancy among different views, numerous successful multi-view support vector machine models have been proposed. However, existing methods face two challenges. Firstly, most multi-view support vector machine models only consider the consensus principle, but ignore the complementarity principle. How to build a novel model with both principles has not been fully considered. Secondly, most multi-view support vector machine models neglect the robustness when the multi-view dataset is contaminated by the noisy samples, error-prone samples and view-inconsistent samples. Considering that the bounded linear-exponential (BLINEX) loss function possesses elegant merits, i.e., asymmetry and boundedness, developing a robust BLINEX-based model is worth exploring. Therefore, in this paper, we propose a BLINEX-based multi-view learning method called MVASY-BX, which explores the consensus and complementarity information with a between-view co-regularization term and importance weights of two views respectively. The mi

Who reads Robust multi-view learning with the bounded LINEX loss?

Author

Jingjing Tang; Hao He; Saiji Fu; Yingjie Tian; Gang Kou; Shan Xu

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)