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Title:Perfect clustering from pairwise comparisons
Author(s):Satpathi, Siddhartha
Advisor(s):Srikant, Rayadurgam
Department / Program:Electrical & Computer Eng
Discipline:Electrical & Computer Engr
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:M.S.
Genre:Thesis
Subject(s):Pairwise comparison
Spectral clustering
Inference
Ranking
Abstract:We consider a pairwise comparisons model with n users and m items. Each user is shown a few pairs of items, and when a pair of items is shown to a user, he or she expresses a preference for one of the items based on a probabilistic model. The goal is to group users into clusters so that users within each cluster have similar preferences. We present an algorithm which clusters all users correctly with high probability using a number of pairwise comparisons which is within a polylog factor of a lower bound.
Issue Date:2017-12-05
Type:Text
URI:http://hdl.handle.net/2142/99229
Rights Information:Copyright 2017 Siddhartha Satpathi
Date Available in IDEALS:2018-03-13
2020-03-14
Date Deposited:2017-12


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