ML-MG: Multi-Label Learning With Missing Labels Using a Mixed Graph

ICCV 2015 Baoyuan WuSiwei LyuBernard Ghanem

This work focuses on the problem of multi-label learning with missing labels (MLML), which aims to label each test instance with multiple class labels given training instances that have an incomplete/partial set of these labels (i.e. some of their labels are missing). To handle missing labels, we propose a unified model of label dependencies by constructing a mixed graph, which jointly incorporates (i) instance-level similarity and class co-occurrence as undirected edges and (ii) semantic label hierarchy as directed edges... (read more)

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