Learning High-Density Regions for a Generalized Kolmogorov-Smirnov Test in High-Dimensional Data

Part of Advances in Neural Information Processing Systems 25 (NIPS 2012)

Bibtex Metadata Paper


Assaf Glazer, Michael Lindenbaum, Shaul Markovitch


We propose an efficient, generalized, nonparametric, statistical Kolmogorov-Smirnov test for detecting distributional change in high-dimensional data. To implement the test, we introduce a novel, hierarchical, minimum-volume sets estimator to represent the distributions to be tested. Our work is motivated by the need to detect changes in data streams, and the test is especially efficient in this context. We provide the theoretical foundations of our test and show its superiority over existing methods.