dc.contributor.advisor | Jonathan A. Kelner. | en_US |
dc.contributor.author | Kishore, Shaunak | en_US |
dc.contributor.other | Massachusetts Institute of Technology. Dept. of Mathematics. | en_US |
dc.date.accessioned | 2013-03-01T15:27:03Z | |
dc.date.available | 2013-03-01T15:27:03Z | |
dc.date.copyright | 2012 | en_US |
dc.date.issued | 2012 | en_US |
dc.identifier.uri | http://hdl.handle.net/1721.1/77534 | |
dc.description | Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science; and, (S.B.)--Massachusetts Institute of Technology, Dept. of Mathematics, 2012. | en_US |
dc.description | Cataloged from PDF version of thesis. | en_US |
dc.description | Includes bibliographical references (p. 18). | en_US |
dc.description.abstract | We obtain improved running times for two algorithms for clustering data: the expectation-maximization (EM) algorithm and Lloyd's algorithm. The EM algorithm is a heuristic for finding a mixture of k normal distributions in Rd that maximizes the probability of drawing n given data points. Lloyd's algorithm is a special case of this algorithm in which the covariance matrix of each normally-distributed component is required to be the identity. We consider versions of these algorithms where the number of mixture components is inferred by assuming a Dirichlet process as a generative model. The separation probability of this process, [alpha], is typically a small constant. We speed up each iteration of the EM algorithm from O(nd2k) to O(ndk log 3(k/a))+nd 2 ) time and each iteration of Lloyd's algorithm from O(ndk) to O(nd(k/a). 39) time. | en_US |
dc.description.statementofresponsibility | by Shaunak Kishore. | en_US |
dc.format.extent | 18 p. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Massachusetts Institute of Technology | en_US |
dc.rights | M.I.T. theses are protected by
copyright. They may be viewed from this source for any purpose, but
reproduction or distribution in any format is prohibited without written
permission. See provided URL for inquiries about permission. | en_US |
dc.rights.uri | http://dspace.mit.edu/handle/1721.1/7582 | en_US |
dc.subject | Electrical Engineering and Computer Science. | en_US |
dc.subject | Mathematics. | en_US |
dc.title | Accelerated clustering through locality-sensitive hashing | en_US |
dc.type | Thesis | en_US |
dc.description.degree | S.B. | en_US |
dc.description.degree | M.Eng. | en_US |
dc.contributor.department | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science | |
dc.contributor.department | Massachusetts Institute of Technology. Department of Mathematics | |
dc.identifier.oclc | 826515141 | en_US |