Timezone: »
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
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)
More from the Same Authors
-
2014 Poster: Approximating Hierarchical MV-sets for Hierarchical Clustering »
Assaf Glazer · Omer Weissbrod · Michael Lindenbaum · Shaul Markovitch -
2013 Poster: q-OCSVM: A q-Quantile Estimator for High-Dimensional Distributions »
Assaf Glazer · Michael Lindenbaum · Shaul Markovitch -
2007 Spotlight: Anytime Induction of Cost-sensitive Trees »
Saher Esmeir · Shaul Markovitch -
2007 Poster: Anytime Induction of Cost-sensitive Trees »
Saher Esmeir · Shaul Markovitch