Robust Multi-Graph Multi-Label Learning With Dual-Granularity Labeling

Multi-graph Multi-label learning ( Mgml ) aims to classify a set of objects of interest, such as text or images, using a bag-of-graphs representation. Previous Mgml works have limitations as they only learn labels at the bag level, lose structural information in learning by transferring graphs into instances, and cannot handle noisy labels. This paper presents a robust coarse and fine-grained Noise Multi-graph Multi-label (cfMGNML) learning framework that builds the learning model over the graphs and empowers label prediction at both the coarse (bag) and fine-grained (graph in each bag) levels with noisy labels. To identify label noise, a label probability matrix is defined to act on the scoring function of each label, with a higher probability value indicating that the label is more likely to be the corresponding graph or bag label. The problem is regularized with the manifold constraint on the label probability matrix to preserve local relationships within the data and uncover its essential manifold structure. Meanwhile, a thresholding rank-loss objective is proposed to rank the labels for the graphs and bags and minimize the hamming loss at one step simultaneously. To tackle the non-convex optimization problem, an effective sub- gradient descent algorithm is developed. Experiments over various datasets demonstrate the proposed method achieves superior performance than the state-of-the-art algorithms.