Multi-instance Multi-graph Dual Embedding Learning

Multi-instance learning concerns about building learning models from a number of labeled instance bags, where each bag consists of instances with unknown labels. A bag is labeled positive if one or more multiple instances inside the bag is positive, and negative otherwise. For all existing multi-instance learning algorithms, they are only applicable to the setting where instances in each bag are represented by a set of well defined feature values. In this paper, we advance the problem to a multi-instance multi-graph setting, where a bag contains a number of instances and graphs in pairs, and the learning objective is to derive classification models from labeled bags, containing both instances and graphs, to predict previously unseen bags with maximum accuracy. To achieve the goal, the main challenge is to properly represent graphs inside each bag and further take advantage of complementary information between instance and graph pairs for learning. In the paper, we propose a Dual Embedding Multi-Instance Multi-Graph Learning (DE-MIMG) algorithm, which employs a dual embedding learning approach to (1) embed instance distributions into the informative sub graphs discovery process, and (2) embed discovered sub graphs into the instance feature selection process. The dual embedding process results in an optimal representation for each bag to provide combined instance and graph information for learning. Experiments and comparisons on real-world multi-instance multi-graph learning tasks demonstrate the algorithm performance.