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Multi-graph multi-label learning (\textsc{Mgml}) is a supervised learningframework, which aims to learn a multi-label classifier from a set of labeledbags each containing a number of graphs. Prior techniques on the \textsc{Mgml}are developed based on transfering graphs into instances and focus on learningthe unseen labels only at the bag level. In this paper, we propose a\textit{coarse} and \textit{fine-grained} Multi-graph Multi-label (cfMGML)learning framework which directly builds the learning model over the graphs andempowers the label prediction at both the \textit{coarse} (aka. bag) level and\textit{fine-grained} (aka. graph in each bag) level. In particular, given aset of labeled multi-graph bags, we design the scoring functions at both graphand bag levels to model the relevance between the label and data using specificgraph kernels. Meanwhile, we propose a thresholding rank-loss objectivefunction to rank the labels for the graphs and bags and minimize thehamming-loss simultaneously at one-step, which aims to addresses the erroraccumulation issue in traditional rank-loss algorithms. To tackle thenon-convex optimization problem, we further develop an effective sub-gradientdescent algorithm to handle high-dimensional space computation required incfMGML. Experiments over various real-world datasets demonstrate cfMGMLachieves superior performance than the state-of-arts algorithms.

Towards Coarse and Fine-grained Multi-Graph Multi-Label Learning