Learning Complex Rare Categories with Dual Heterogeneity.

In the era of big data, it is often the case that the selfsimilar rare categories in a large data set are of great importance, such as the malicious insiders in big organizations, and the IC devices with defects in semiconductor manufacturing. Furthermore, such rare categories often exhibit multiple types of heterogeneity, such as the task heterogeneity, which originates from data collected in multiple domains, and the view heterogeneity, which originates from multiple information sources. Existing methods for learning rare categories mainly focus on the homogeneous settings, i.e., a single task and a single view. In this paper, for the first time, we study complex rare categories with both task and view heterogeneity, and propose a novel optimization framework named MLID. It introduces a boundary characterization metric to capture the sharp changes in density near the boundary of the rare categories in the feature space, and constructs a graph-based model to leverage both task and view heterogeneity. Furthermore, MLID integrates them in a way of mutual benefit. We also present an effective algorithm to solve this framework, analyze its performance from various aspects, and demonstrate its effectiveness on both synthetic and real datasets.