Learning High-Density Regions for a Generalized Kolmogorov-Smirnov Test in High-Dimensional Data
Abstract
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.
Cite
Text
Glazer et al. "Learning High-Density Regions for a Generalized Kolmogorov-Smirnov Test in High-Dimensional Data." Neural Information Processing Systems, 2012.Markdown
[Glazer et al. "Learning High-Density Regions for a Generalized Kolmogorov-Smirnov Test in High-Dimensional Data." Neural Information Processing Systems, 2012.](https://mlanthology.org/neurips/2012/glazer2012neurips-learning/)BibTeX
@inproceedings{glazer2012neurips-learning,
title = {{Learning High-Density Regions for a Generalized Kolmogorov-Smirnov Test in High-Dimensional Data}},
author = {Glazer, Assaf and Lindenbaum, Michael and Markovitch, Shaul},
booktitle = {Neural Information Processing Systems},
year = {2012},
pages = {728-736},
url = {https://mlanthology.org/neurips/2012/glazer2012neurips-learning/}
}