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Classifying multi-label instances using incompletely labeled instances is one of the fundamental tasks in multi-label learning. Most existing methods regard this task as supervised weak-label learning problem and assume sufficient partially labeled instances are available. However, collecting or annotating such instances is expensive and time-consuming. In contrast, abundant unlabeled instances are...
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