Multi-view Data Classification with a Label-driven Auto-weighted Strategy

TL;DR

This paper introduces a label-driven auto-weighted strategy for semi-supervised multi-view classification, effectively evaluating view importance from label information to improve accuracy and robustness against low-quality views.

Contribution

It proposes a novel auto-weighted strategy that links labeled data to view importance, enhancing multi-view learning by reducing the influence of low-quality views.

Findings

Achieves optimal or near-optimal classification accuracy.

Distinguishes view importance more accurately than existing strategies.

Operates with low computational cost.

Abstract

Distinguishing the importance of views has proven to be quite helpful for semi-supervised multi-view learning models. However, existing strategies cannot take advantage of semi-supervised information, only distinguishing the importance of views from a data feature perspective, which is often influenced by 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 an auto-weighted strategy to evaluate the importance of views from a label 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. The initialization of the proposed model can be effectively determined by labeled data, which is practical. The model is decoupled into three small-scale sub-problems that can efficiently be optimized with a local convergence guarantee. The experimental results on classification tasks show that the proposed method achieves optimal or sub-optimal classification accuracy at the lowest 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 on multi-view datasets with low-quality views.