Learning high-density regions for a generalized kolmogorov-smirnov test in high-dimensional data

Assaf Glazer; Michael Lindenbaoum; Shaul Markovitch

Learning high-density regions for a generalized kolmogorov-smirnov test in high-dimensional data

Assaf Glazer, Michael Lindenbaoum, Shaul Markovitch

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

12 Scopus citations

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.

Original language	English
Title of host publication	Advances in Neural Information Processing Systems 25
Subtitle of host publication	26th Annual Conference on Neural Information Processing Systems 2012, NIPS 2012
Pages	728-736
Number of pages	9
State	Published - 1 Dec 2012
Externally published	Yes
Event	26th Annual Conference on Neural Information Processing Systems 2012, NIPS 2012 - Lake Tahoe, NV, United States Duration: 3 Dec 2012 → 6 Dec 2012

Publication series

Name	Advances in Neural Information Processing Systems
Volume	1
ISSN (Print)	1049-5258

Conference

Conference	26th Annual Conference on Neural Information Processing Systems 2012, NIPS 2012
Country/Territory	United States
City	Lake Tahoe, NV
Period	3/12/12 → 6/12/12

ASJC Scopus subject areas

Computer Networks and Communications
Information Systems
Signal Processing

Cite this

@inproceedings{b8486e0ea391439599f7c36c897b093c,

title = "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.",

author = "Assaf Glazer and Michael Lindenbaoum and Shaul Markovitch",

year = "2012",

month = dec,

day = "1",

language = "English",

isbn = "9781627480031",

series = "Advances in Neural Information Processing Systems",

pages = "728--736",

booktitle = "Advances in Neural Information Processing Systems 25",

note = "26th Annual Conference on Neural Information Processing Systems 2012, NIPS 2012 ; Conference date: 03-12-2012 Through 06-12-2012",

}

Glazer, A, Lindenbaoum, M & Markovitch, S 2012, Learning high-density regions for a generalized kolmogorov-smirnov test in high-dimensional data. in Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012, NIPS 2012. Advances in Neural Information Processing Systems, vol. 1, pp. 728-736, 26th Annual Conference on Neural Information Processing Systems 2012, NIPS 2012, Lake Tahoe, NV, United States, 3/12/12.

Learning high-density regions for a generalized kolmogorov-smirnov test in high-dimensional data. / Glazer, Assaf; Lindenbaoum, Michael; Markovitch, Shaul.
Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012, NIPS 2012. 2012. p. 728-736 (Advances in Neural Information Processing Systems; Vol. 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Markovitch, Shaul

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N2 - 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.

AB - 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.

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M3 - Conference contribution

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Learning high-density regions for a generalized kolmogorov-smirnov test in high-dimensional data

Abstract

Publication series

Conference

ASJC Scopus subject areas

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