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Poster
Learning High-Density Regions for a Generalized Kolmogorov-Smirnov Test in High-Dimensional Data
Assaf Glazer · Michael Lindenbaum · Shaul Markovitch
Tue Dec 04 07:00 PM -- 12:00 AM (PST) @ Harrah’s Special Events Center 2nd Floor #None
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.
Author Information
Assaf Glazer (Technion)
Michael Lindenbaum (Technion)
Shaul Markovitch (Technion)
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