ml-lab-sau/Multi-label-learning-with-missing-labels-using-sparse-global-structure-for-label-specific-features

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.

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