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87005 |
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|a dc
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|a Indyk, Piotr
|e author
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|a Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
|e contributor
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|a Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
|e contributor
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|a Indyk, Piotr
|e contributor
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|a Rubinfeld, Ronitt
|e contributor
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|a Levi, Reut
|e author
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|a Rubinfeld, Ronitt
|e author
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|a Approximating and testing k-histogram distributions in sub-linear time
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|b Association for Computing Machinery (ACM),
|c 2014-05-15T18:13:10Z.
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|z Get fulltext
|u http://hdl.handle.net/1721.1/87005
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|a A discrete distribution p, over [n], is a k histogram if its probability distribution function can be represented as a piece-wise constant function with k pieces. Such a function is represented by a list of k intervals and k corresponding values. We consider the following problem: given a collection of samples from a distribution p, find a k-histogram that (approximately) minimizes the l [subscript 2] distance to the distribution p. We give time and sample efficient algorithms for this problem. We further provide algorithms that distinguish distributions that have the property of being a k-histogram from distributions that are ε-far from any k-histogram in the l [subscript 1] distance and l [subscript 2] distance respectively.
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|a David & Lucile Packard Foundation (Fellowship)
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|a National Science Foundation (U.S.) (Grant CCF-0728645)
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|a National Science Foundation (U.S.) (Grant 0732334)
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|a National Science Foundation (U.S.) (Grant 0728645)
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|a en_US
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|a Article
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|t Proceedings of the 31st symposium on Principles of Database Systems (PODS '12)
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