Multi-View Partial Multi-Label Learning via Graph-Fusion-Based Label Enhancement

Abstract

Multi-view partial multi-label learning (MVPML) aims to learn a multi-label predictive model from the training examples, each of which is presented by multiple feature vectors while associated with a set of candidate labels where only a subset is correct. Generally, existing techniques work simply by identifying the ground-truth label via aggregating the features from all views to train a final classifier, but ignore the cause of the incorrect labels in the candidate label sets, i.e., the diverse property of the representation from different views leads to the incorrect labels which form the candidate labels alone with the essential supervision. In this paper, a novel MVPML approach is proposed to learn the predictive model and the incorrect-labeling model jointly by incorporating the graph-fusion-based topological structure of the feature space. Specifically, the latent label distribution and the incorrect labels are identified simultaneously in a unified framework under the supervision of candidate labels. In addition, a common topological structure of the feature space from all views is learned via the graph fusion for further capturing the latent label distribution. Experimental results on the real-world datasets clearly validate the effectiveness of the proposed approach for solving multi-view partial multi-label learning problems.