Staff View
Supervised feature learning via dependency maximization

Descriptive

TitleInfo
Title
Supervised feature learning via dependency maximization
Name (type = personal)
NamePart (type = family)
Tonde
NamePart (type = given)
Chetan J.
NamePart (type = date)
1985-
DisplayForm
Chetan J. Tonde
Role
RoleTerm (authority = RULIB)
author
Name (type = personal)
NamePart (type = family)
Elgammal
NamePart (type = given)
Ahmed
DisplayForm
Ahmed Elgammal
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
chair
Name (type = personal)
NamePart (type = family)
Awasthi
NamePart (type = given)
Pranjal
DisplayForm
Pranjal Awasthi
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Eliassi-Rad
NamePart (type = given)
Tina
DisplayForm
Tina Eliassi-Rad
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Dicker
NamePart (type = given)
Lee
DisplayForm
Lee Dicker
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)
2016-05
CopyrightDate (encoding = w3cdtf); (qualifier = exact)
2016
Place
PlaceTerm (type = code)
xx
Language
LanguageTerm (authority = ISO639-2b); (type = code)
eng
Abstract (type = abstract)
A key challenge in machine learning is to automatically extract relevant feature representations of data for a given task. This becomes especially formidable task for structured data like images, which are often highly structured and complex. In this thesis, we propose frameworks for supervised feature learning for structured and unstructured data, via dependency maximization. In the first part of this dissertation we look at the problem of learning kernels for structured prediction. We present a novel framework called Twin Kernel Learning which proposes the idea of polynomial expansions of kernels, to learn kernels over structured data so as to maximize a dependency criterion called Hilbert-Schmidt Independence criterion (HSIC). We also give an efficient, matrix-decomposition based algorithm for learning these expansions and use it to learn covariance kernels of Twin Gaussian Processes. We demonstrate state-of-the-art empirical results on several synthetic and real-world datasets. In the second part of this work, we present a novel framework for supervised dimensionality reduction based on a dependency criterion called Distance Correlation. Our framework is based on learning low-dimensional features which maximize squared sum of Distance Correlations of low dimensional features, with both, the response, and the covariates. We propose a novel algorithm to maximize our proposed objective, and also show superior empirical results over state-of-the-art on multiple datasets.
Subject (authority = RUETD)
Topic
Computer Science
Subject (authority = ETD-LCSH)
Topic
Machine learning
Subject (authority = ETD-LCSH)
Topic
Kernel functions
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_7169
PhysicalDescription
Form (authority = gmd)
electronic resource
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
1 online resource (xii, 89 p. : ill.)
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
Note (type = statement of responsibility)
by Chetan J. Tonde
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/T3H997DJ
Back to the top

Rights

RightsDeclaration (ID = rulibRdec0006)
The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Tonde
GivenName
Chetan
MiddleName
J.
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2016-04-11 21:18:53
AssociatedEntity
Name
Chetan Tonde
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.
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-1.40.15
DateCreated (point = end); (encoding = w3cdtf); (qualifier = exact)
2016-04-11T21:01:05
DateCreated (point = end); (encoding = w3cdtf); (qualifier = exact)
2016-04-11T21:01:05
Back to the top
Version 8.3.13
Rutgers University Libraries - Copyright ©2020