Multi-Graph Multi-Label Learning Based on Entropy.

Recently, was proposed as the extension of and has achieved some successes. However, to the best of our knowledge, currently, there is no study working on , where each object is represented as a bag containing a number of graphs and each bag is marked with multiple class labels. It is an interesting problem existing in many applications, such as image classification, medicinal analysis and so on. In this paper, we propose an innovate algorithm to address the problem. Firstly, it uses more precise structures, multiple , instead of to represent an image so that the classification accuracy could be improved. Then, it uses multiple labels as the output to eliminate the semantic ambiguity of the image. Furthermore, it calculates the entropy to mine the informative subgraphs instead of just mining the frequent subgraphs, which enables selecting the more accurate features for the classification. Lastly, since the current algorithms cannot directly deal with graph-structures, we degenerate the into the in order to solve it by . The performance study shows that our algorithm outperforms the competitors in terms of both effectiveness and efficiency.