test Browse by Author Names Browse by Titles of Works Browse by Subjects of Works Browse by Issue Dates of Works

Advanced Search
& Collections
Issue Date   
Sign on to:   
Receive email
My Account
authorized users
Edit Profile   
About T-Space   

T-Space at The University of Toronto Libraries >
School of Graduate Studies - Theses >
Doctoral >

Please use this identifier to cite or link to this item: http://hdl.handle.net/1807/27587

Title: Message Passing Algorithms for Facility Location Problems
Authors: Lazic, Nevena
Advisor: Aarabi, Parham
Frey, Brendan J.
Department: Electrical and Computer Engineering
Keywords: probabilistic graphical models
facility location
MAP inference
Issue Date: 9-Jun-2011
Abstract: Discrete location analysis is one of the most widely studied branches of operations research, whose applications arise in a wide variety of settings. This thesis describes a powerful new approach to facility location problems - that of message passing inference in probabilistic graphical models. Using this framework, we develop new heuristic algorithms, as well as a new approximation algorithm for a particular problem type. In machine learning applications, facility location can be seen a discrete formulation of clustering and mixture modeling problems. We apply the developed algorithms to such problems in computer vision. We tackle the problem of motion segmentation in video sequences by formulating it as a facility location instance and demonstrate the advantages of message passing algorithms over current segmentation methods.
URI: http://hdl.handle.net/1807/27587
Appears in Collections:Doctoral

Files in This Item:

File Description SizeFormat
Lazic_Nevena_201103_PhD-thesis.pdf4.72 MBAdobe PDF

Items in T-Space are protected by copyright, with all rights reserved, unless otherwise indicated.