A semi-supervised label-driven auto-weighted strategy for multi-view data classification

Abstract

Distinguishing the importance of views plays a key role in multi-view learning as each view often contributes differently to a specific task. However, existing strategies generally attach importance to each view blindly from a data representation perspective, which often fails to identify low-quality views then leading to poor performance. In this paper, by establishing a link between labeled data and the importance of different views, we propose a semi-supervised label-driven auto-weighted strategy that evaluates the importance of views from a labeling perspective to avoid the negative impact of unimportant or low-quality views. Based on this strategy, we propose a transductive semi-supervised auto-weighted multi-view classification model that learns the labels of multi-view data under the more reasonable weights of the views. The model is decoupled into three small-scale sub-problems that can efficiently be optimized. The experimental results on classification tasks show that our proposed method achieves very promising classification accuracy at a lower computational cost compared to other related methods, and the weight change experiments show that our proposed strategy can distinguish view importance more accurately than other related strategies especially when low-quality views are involved in multi-view learning.