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Weight matrix sharing for multi-label learning
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DOI：10.1016/j.patcog.2022.109156.png)
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Abstract
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Multi-label learning on real-world data is a challenging task due to sparse labels, missing labels, and sparse structures. Some existing approaches are effective in addressing the former two issues. In this pa-per, we propose a shared weight matrix with low-rank and sparse regularization for multi-label learning (2SML) algorithm to address the issues simultaneously. First, two explicit correlation matrices are con-structed from the feature matrix and label matrix. Second, we select informative labels by instance rep-resentativeness to learn implicit correlations. Third, a feature manifold and label manifold are employed to guide the shared weight learning process. Extensive experiments are undertaken on multiple bench-mark datasets with and without missing labels. The results show that the proposed method outperforms the state-of-the-art methods.(c) 2022 Elsevier Ltd. All rights reserved.
Keywords:
Low-rank
Missing labels
Multi -label learning
Shared weight
Sparse
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