To deal with the issues emerging from incomplete labels and high-dimensional input space, we propose a multi-label learning approach based on identifying the label-specific features and constraining them with a sparse global structure. The sparse structural constraint helps maintain the typical characteristics of the multi-label learning data.
multi-label-learning missing-labels sparse-global-structure auxiliary-label-correlations label-specific-features
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Updated
Jan 28, 2024 - MATLAB