2026-09-19T12:16:44Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/176712022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Santosh Vempala.
Zhou, Hanson M. (Hanson Mi), 1977-
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2005-06-02T16:38:38Z
2005-06-02T16:38:38Z
2003
2004
http://hdl.handle.net/1721.1/17671
55677179
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, February 2004.
Includes bibliographical references (leaves 26-27).
Given a set of n points with a matrix of pairwise similarity measures, one would like to partition the points into clusters so that similar points are together and different ones apart. We present an algorithm requiring only matrix exponentiation that performs well in practice and bears an elegant interpretation in terms of random walks on a graph. Under a certain mixture model involving planting a partition via randomized rounding of tailored matrix entries, the algorithm can be proven effective for only a single squaring. It is shown that the clustering performance of the algorithm degrades with larger values of the exponent, thus revealing that a single squaring is optimal.
by Hanson M. Zhou.
S.M.
27 leaves
1209849 bytes
1210232 bytes
application/pdf
application/pdf
eng
Massachusetts Institute of Technology
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http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Clustering via matrix exponentiation
Thesis
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