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Edge-friendly distributed PCA
Descriptive
TitleInfo
Title
Edge-friendly distributed PCA
Name
(type = personal)
NamePart
(type = family)
Xiang
NamePart
(type = given)
Bingqing
NamePart
(type = date)
1996-
DisplayForm
Bingqing Xiang
Role
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author
Name
(type = personal)
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(type = family)
Bajwa
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(type = given)
Waheed
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Waheed Bajwa
Affiliation
Advisory Committee
Role
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chair
Name
(type = personal)
NamePart
(type = family)
Yuan
NamePart
(type = given)
Bo
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Bo Yuan
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Advisory Committee
Role
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internal member
Name
(type = personal)
NamePart
(type = family)
Zhang
NamePart
(type = given)
Yuqian
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Yuqian Zhang
Affiliation
Advisory Committee
Role
RoleTerm
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internal member
Name
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NamePart
Rutgers University
Role
RoleTerm
(authority = RULIB)
degree grantor
Name
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NamePart
School of Graduate Studies
Role
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school
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Text
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theses
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2020
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(type = degree)
2020-05
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2020
Language
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(authority = ISO 639-3:2007);
(type = text)
English
Abstract
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Big, distributed data create a bottleneck for storage and computation in machine learn- ing. Principal Component Analysis (PCA) is a dimensionality reduction tool to resolve the issue. This thesis considers how to estimate the principal subspace in a loosely connected network for data in a distributed setting. The goal for PCA is to extract the essential structure of the dataset. The traditional PCA requires a data center to aggregate all data samples and proceed with calculation. However, in real-world settings, where memory, storage, and communication constraints are an issue, it is sometimes impossible to gather all the data in one place. The intuitive approach is to compute the PCA in a decentralized manner. The focus of this thesis is to find a lower-dimensional representation of the distributed data with the well-known orthogonal iteration algorithm. The proposed distributed PCA algorithm estimates the subspace representation from sample covariance matrices in a decentralized network while preserving the privacy of the local data.
Subject
(authority = local)
Topic
Distributed
Subject
(authority = LCSH)
Topic
Electronic data processing -- Distributed processing
Subject
(authority = RUETD)
Topic
Electrical and Computer Engineering
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Title
Rutgers University Electronic Theses and Dissertations
Identifier
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ETD
Identifier
ETD_10824
PhysicalDescription
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application/pdf
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text/xml
Extent
1 online resource (vii, 42 pages) : illustrated
Note
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M.S.
Note
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Includes bibliographical references
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Title
School of Graduate Studies Electronic Theses and Dissertations
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rucore10001600001
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NjNbRU
Identifier
(type = doi)
doi:10.7282/t3-3mx0-5s88
Genre
(authority = ExL-Esploro)
ETD graduate
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Rights
RightsDeclaration
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The author owns the copyright to this work.
RightsHolder
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Name
FamilyName
Xiang
GivenName
Bingqing
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime
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2020-04-24 21:24:21
AssociatedEntity
Name
Bingqing Xiang
Role
Copyright holder
Affiliation
Rutgers University. School of Graduate Studies
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
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Technical
RULTechMD
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