Staff View
Generalized distributed learning under uncertainty for camera networks

Descriptive

TitleInfo
Title
Generalized distributed learning under uncertainty for camera networks
Name (type = personal)
NamePart (type = family)
Yoon
NamePart (type = given)
Sejong
NamePart (type = date)
1980-
DisplayForm
Sejong Yoon
Role
RoleTerm (authority = RULIB)
author
Name (type = personal)
NamePart (type = family)
Pavlovic
NamePart (type = given)
Vladimir
DisplayForm
Vladimir Pavlovic
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
chair
Name (type = personal)
NamePart (type = family)
Kapadia
NamePart (type = given)
Mubbasir
DisplayForm
Mubbasir Kapadia
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Metaxas
NamePart (type = given)
Dimitris
DisplayForm
Dimitris Metaxas
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Badler
NamePart (type = given)
Norman
DisplayForm
Norman Badler
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
outside member
Name (type = corporate)
NamePart
Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
Name (type = corporate)
NamePart
Graduate School - New Brunswick
Role
RoleTerm (authority = RULIB)
school
TypeOfResource
Text
Genre (authority = marcgt)
theses
OriginInfo
DateCreated (qualifier = exact)
2016
DateOther (qualifier = exact); (type = degree)
2016-10
CopyrightDate (encoding = w3cdtf); (qualifier = exact)
2016
Place
PlaceTerm (type = code)
xx
Language
LanguageTerm (authority = ISO639-2b); (type = code)
eng
Abstract (type = abstract)
Consensus-based distributed learning is a machine learning technique used to find the general consensus of local learning models to achieve a global objective. It is an important problem with increasing level of interest due to its applications in sensor networks. There are many benefits of distributed learning over traditional centralized learning, such as faster computation and reduced communication cost. In this dissertation, we focus on the merit that distributed learning can be performed in a fully decentralized way, which makes it one step further different from parallel computing approaches. First, we propose a general distributed probabilistic learning framework based on distributed optimization using an Alternating Direction Method of Multipliers (ADMM). We show that it can be applied to computer vision algorithms which have traditionally assumed a centralized computational setting. We demonstrate that our probabilistic interpretation of the decentralized processing is useful in dealing with missing values which are not explicitly handled in prior works. We provide empirical evaluations on a computer vision problem termed distributed affine structure from motion (SfM). Second, we propose two useful extensions of the distributed probabilistic learning framework. We first extend our framework so that it can incrementally update the learned model in an online fashion. To do this, we propose to use a Bayesian inference model based on Bregman ADMM (B-ADMM). Next, we show that the distributed learning tasks can be carried out more rapidly by introducing smart update strategies to the underlying ADMM optimization algorithm. By adaptively balancing primal and dual residuals of ADMM, we demonstrate an improved empirical convergence speed in a fully decentralized setting, without limiting the application range of ADMM-based optimization. Finally, we introduce a potential application of consensus-based distributed optimization on the human trajectory estimation problem. We formulate the trajectory estimation problem as a global optimization issue with constraints encoding various prior conditions that can be either allowed or forbidden in real world situations. We show that our method can effectively estimate the noisy, corrupted trajectories from off-the-shelf human trackers that could assist in human crowd analysis and simulation.
Subject (authority = RUETD)
Topic
Computer Science
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_7513
PhysicalDescription
Form (authority = gmd)
electronic resource
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
1 online resource (xii, 119 p. : ill.)
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
Subject (authority = ETD-LCSH)
Topic
Machine learning
Note (type = statement of responsibility)
by Sejong Yoon
RelatedItem (type = host)
TitleInfo
Title
Graduate School - New Brunswick Electronic Theses and Dissertations
Identifier (type = local)
rucore19991600001
Location
PhysicalLocation (authority = marcorg); (displayLabel = Rutgers, The State University of New Jersey)
NjNbRU
Identifier (type = doi)
doi:10.7282/T33B62FD
Genre (authority = ExL-Esploro)
ETD doctoral
Back to the top

Rights

RightsDeclaration (ID = rulibRdec0006)
The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Yoon
GivenName
Sejong
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2016-08-25 15:12:47
AssociatedEntity
Name
Sejong Yoon
Role
Copyright holder
Affiliation
Rutgers University. Graduate School - New Brunswick
AssociatedObject
Type
License
Name
Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
RightsEvent
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2016-10-31
DateTime (encoding = w3cdtf); (qualifier = exact); (point = end)
2017-05-02
Type
Embargo
Detail
Access to this PDF has been restricted at the author's request. It will be publicly available after May 2nd, 2017.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
Back to the top

Technical

RULTechMD (ID = TECHNICAL1)
ContentModel
ETD
OperatingSystem (VERSION = 5.1)
windows xp
CreatingApplication
Version
1.5
ApplicationName
pdfTeX-1.40.16
DateCreated (point = end); (encoding = w3cdtf); (qualifier = exact)
2016-08-28T03:50:26
DateCreated (point = end); (encoding = w3cdtf); (qualifier = exact)
2016-08-28T03:50:26
Back to the top
Version 8.5.5
Rutgers University Libraries - Copyright ©2024