Clustering via matrix exponentiation
Name
55677179-MIT.pdf
Description
Full printable version
Size
1.15 MB
Format
Adobe PDF
Checksum (MD5)
c19492f8ee19b16bba114600ddea6bd6
Author(s)
Zhou, Hanson M. (Hanson Mi), 1977-
Advisor(s)
Santosh Vempala.
Date Issued
2004
Publisher
Massachusetts Institute of Technology
Abstract
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.
Description
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, February 2004.
Includes bibliographical references (leaves 26-27).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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