Double missing multi-view multi-label classification via an attention-guided multi-space consistency alignment framework

Abstract

Multi-view multi-label classification (MVMLC) seeks to enhance classification by integrating diverse data views, but its practical use is hindered by missing views and labels, posing the significant challenge of incomplete MVMLC(IMVMLC). Although various IMVMLC approaches have been proposed, most of them handle multiple objectives in a single feature space and thus overlook the conflict between learning consistent common semantics and reconstructing view-specific information. In addition, existing multi-view classification methods mainly consider utilizing the features of each view, while ignoring the inconsistent contributions of each view and usually relying on static average weighting strategies. To this end, we propose our Attention-Guided MultiSpace Consistency Alignment Framework (AMCA). In Stage 1, AMCA introduces multi-space representation learning with dual-level contrastive objectives, explicitly disentangling shared and view-specific semantics to resolve the objective conflict and yield more informative embeddings. In Stage 2, AMCA employs an attention-guided fusion module that dynamically evaluates and integrates multi-view features based on their relevance to the classification task, enabling robust decision-making even with missing data. Extensive experiments validate the effectiveness and superiority of our proposal.