Multi-label weak-label learning via semantic reconstruction and label correlations

In the multi-label classification task, an instance is simultaneously associated with multi-ple semantic labels. Due to the high complexity of the semantic space in practical applica-tions, obtaining instances with full labels is difficult, leading to the weak-label problem. Existing methods focus on the low-rank and instance manifold regularization assumptions of the label matrix to recover the ground-truth label matrix but ignore the influence that the above assumptions may not hold due to the semantic noise caused by missing labels. To address the problem, this paper proposes a simple and effective method to recover the label space by reconstructing the label semantic space through joint label correlation to solve the multi-label weak-label classification task. Specifically, we leverage the label information consistency and feature-label dependency assumptions to reconstruct the semantic space and consider label correlations to enhance the information of semantic views. Moreover, l2;1-norm is utilized to mitigate the effect of missing label space noise. Additionally, the linear model of the proposed method is expanded to a nonlinear version of the kernel method to address the problem of the inseparability of linear data. Extensive experiments on several real-world tasks show that the proposed method outperforms some state-of-the-art methods.(c) 2022 Elsevier Inc. All rights reserved.