Fast One-class Classification using Class Boundary-preserving Random Projections
Recommended citation: Bhattacharya, A., Varambally, S., Bagchi, A., & Bedathur, S. (2021, August). Fast One-class Classification using Class Boundary-preserving Random Projections. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (pp. 66-74).
Published in: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 2021
We develop a novel fast one-class classifier called FROCC. We generate a large number of random directions, and project each point from the training set onto these directions. Along each direction, we group “nearby” projections into intervals. Given a test point, we score it as an inlier or outlier based on the proportion of directions along which it lies in an interval.