抄録
Identifying atypical objects is one of the classic tasks in machine learning. Recent works, e.g., One-class Clustering and Minority Detection, have explored the task further to identify clusters of atypical objects which strongly contrast from the rest of the dataset. In such problems, avoiding false positive detection is an important yet significantly difficult issue. In this paper, we propose an information theoretic clustering which aims to compactly represent the global and local structures of the dataset and identify atypical clusters in terms of information geometric distance. The former objective contributes to reducing the number of false positive detections. Its formalization further yields a unifying view of the classic outlier detection and the novel tasks. We present a scalable algorithm for detecting multiple clusters of atypical objects without a pre-defined number of clusters. The algorithm is evaluated as an unsupervised two-class classification using simulated datasets and a text classification benchmark.
