电竞博彩-电子竞技博彩

Robust multi-view learning via adaptive regression

Bingbing Jiang，Junhao Xiang，Xingyu Wu，Yadi Wang，Huanhuan Chen，Weiwei Cao，Weiguo Sheng

//doi.org/10.1016/j.ins.2022.08.017Get 

Abstract

As data collected from different sources have multiple representations, multi-view learning has become an important paradigm of machine learning. To exploit multi-view data, previous works either tackle each view separately or concatenate all views directly, such that the distinctions as well as correlations of different views are often ignored. Furthermore, existing models usually involve intractable parameters that need to be manually determined to balance the contributions of different views, degrading the efficiency and applicability of models. In this paper, a novel multi-view learning framework, namely Robust Multi-view learning via Adaptive Regression (RMAR), is derived to discriminate diverse views in a self-supervised weighting manner without extra parameters. Meanwhile, RMAR coalesces multiple feature projections with adaptive view-wise weights and adopts L2,1-norm regression loss to learn a joint projection subspace compatible across all views, not only increasing the robustness of model but also preserving the consistency and diversity among views. Furthermore, RMAR can be naturally extended for feature selection by imposing L2,1-norm constraint on feature projections. Additionally, an efficient convergent algorithm is developed to solve RMAR. Extensive experiments have been performed to validate the effectiveness of RMAR for classification and feature selection and show its superiority over state-of-the-arts.