Incomplete Multi-View Multi-Label Learning via Label-Guided Masked View- and Category-Aware Transformers

• URL: http://arxiv.org/abs/2303.07180v1
• Date: Mon, 13 Mar 2023 15:22:50 GMT
• Title: Incomplete Multi-View Multi-Label Learning via Label-Guided Masked View- and Category-Aware Transformers
• Authors: Chengliang Liu, Jie Wen, Xiaoling Luo, Yong Xu
• Abstract summary: We propose a general multi-view multi-label learning framework named label-guided masked view- and category-aware transformers. Considering the imbalance of expressive power among views, an adaptively weighted view fusion module is proposed to obtain view-consistent embedding features.
• Score: 19.720564730308993
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As we all know, multi-view data is more expressive than single-view data and multi-label annotation enjoys richer supervision information than single-label, which makes multi-view multi-label learning widely applicable for various pattern recognition tasks. In this complex representation learning problem, three main challenges can be characterized as follows: i) How to learn consistent representations of samples across all views? ii) How to exploit and utilize category correlations of multi-label to guide inference? iii) How to avoid the negative impact resulting from the incompleteness of views or labels? To cope with these problems, we propose a general multi-view multi-label learning framework named label-guided masked view- and category-aware transformers in this paper. First, we design two transformer-style based modules for cross-view features aggregation and multi-label classification, respectively. The former aggregates information from different views in the process of extracting view-specific features, and the latter learns subcategory embedding to improve classification performance. Second, considering the imbalance of expressive power among views, an adaptively weighted view fusion module is proposed to obtain view-consistent embedding features. Third, we impose a label manifold constraint in sample-level representation learning to maximize the utilization of supervised information. Last but not least, all the modules are designed under the premise of incomplete views and labels, which makes our method adaptable to arbitrary multi-view and multi-label data. Extensive experiments on five datasets confirm that our method has clear advantages over other state-of-the-art methods.

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