VADIS: Investigating Inter-View Representation Biases for Multi-View Partial Multi-Label Learning

Jie Wang, Ning Xu, Xin Geng

Keywords: Multi-label learning, partial label learning, disambiguation

Abstract: Multi-view partial multi-label learning (MVPML) deals with training data where each example is represented by multiple feature vectors and associated with a set of candidate labels, only a subset of which are correct. The diverse representation biases present in different views complicate the annotation process in MVPML, leading to the inclusion of incorrect labels in the candidate label set. Existing methods typically merge features from different views to identify the correct labels in the training data without addressing the representation biases inherent in different views. In this paper, we propose a novel MVPML method called \textsc{Vadis}, which investigates view-aware representations for disambiguation and predictive model learning. Specifically, we exploit the global common representation shared by all views, aligning it with a local semantic similarity matrix to estimate ground-truth labels via a low-rank mapping matrix. Additionally, to identify incorrect labels, the view-specific inconsistent representation is recovered by leveraging the sparsity assumption. Experiments on real-world datasets validate the superiority of our approach over other state-of-the-art methods.

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Submission Number: 362