Unsupervised Feature Selection with Feature Clustering

As an effective technique for dimensionality reduction, feature selection has a broad application in different research areas. In this paper, we present a feature selection method based on a novel feature clustering procedure, which aims at partitioning the features into different clusters such that...

Full description

Saved in:
Bibliographic Details
Published in:2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology Vol. 1; pp. 9 - 15
Main Authors: Cheung, Yiu-Ming, Jia, Hong
Format: Conference Proceeding
Language:English
Published: IEEE 01.12.2012
Subjects:
Clustering algorithms
Computational modeling
Feature Clustering
Feature extraction
Feature Redundancy
Filters
High dimensional data
Laplace equations
Number of Features
Partitioning algorithms
Principal component analysis
Redundancy
Unsupervised Feature Selection
Vectors
ISBN:9781467360579, 1467360570
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Abstract As an effective technique for dimensionality reduction, feature selection has a broad application in different research areas. In this paper, we present a feature selection method based on a novel feature clustering procedure, which aims at partitioning the features into different clusters such that the features in the same cluster contain similar structural information of the given instances. Subsequently, since the obtained feature subset consists of features from variant clusters, the similarity between selected features will be low. This allows us to reserve the most data structural information with the minimum number of features. Experimental results on different benchmark data sets demonstrate the superiority of the proposed method.
AbstractList As an effective technique for dimensionality reduction, feature selection has a broad application in different research areas. In this paper, we present a feature selection method based on a novel feature clustering procedure, which aims at partitioning the features into different clusters such that the features in the same cluster contain similar structural information of the given instances. Subsequently, since the obtained feature subset consists of features from variant clusters, the similarity between selected features will be low. This allows us to reserve the most data structural information with the minimum number of features. Experimental results on different benchmark data sets demonstrate the superiority of the proposed method.
Author Jia, Hong
Cheung, Yiu-Ming
Author_xml – sequence: 1
  givenname: Yiu-Ming
  surname: Cheung
  fullname: Cheung, Yiu-Ming
  email: ymc@comp.hkbu.edu.hk
  organization: Dept. of Comput. Sci., Hong Kong Baptist Univ., Hong Kong, China
– sequence: 2
  givenname: Hong
  surname: Jia
  fullname: Jia, Hong
  email: hjia@comp.hkbu.edu.hk
  organization: Dept. of Comput. Sci., Hong Kong Baptist Univ., Hong Kong, China
BookMark eNo9jE1Lw0AUAFdU0NacPXjJH0h8L_t9LMFqoODBFo9lk7zVlZiW3UTx3ysowsDAHGbBzsbDSIxdI5SIYG-fm6JZbcsKsCoraU_YArSyUhgD6pRlVhsUSnMFUtsLlqX0BgAIXCLgJVO7Mc1Hih8hUZ-vyU1zpPyJBuqmcBjzzzC9_ud6mNNEMYwvV-zcuyFR9ucl263vtvVDsXm8b-rVpgio5VT0lhCAU_uDbiU6b5TyTotW9ZqMUQLRd7wDJ5wAIY3giJ0Swntpve74kt38fgMR7Y8xvLv4tVcS0UjLvwEvqEh9
CODEN IEEPAD
ContentType Conference Proceeding
DBID 6IE
6IL
CBEJK
RIE
RIL
DOI 10.1109/WI-IAT.2012.259
DatabaseName IEEE Electronic Library (IEL) Conference Proceedings
IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume
IEEE Xplore All Conference Proceedings
IEEE Electronic Library (IEL)
IEEE Proceedings Order Plans (POP All) 1998-Present
DatabaseTitleList
Database_xml – sequence: 1
  dbid: RIE
  name: IEEE Electronic Library (IEL)
  url: https://ieeexplore.ieee.org/
  sourceTypes: Publisher
DeliveryMethod fulltext_linktorsrc
Discipline Computer Science
EISBN 0769548806
9780769548807
EndPage 15
ExternalDocumentID 6511859
Genre orig-research
GroupedDBID 6IE
6IL
ACM
ALMA_UNASSIGNED_HOLDINGS
APO
CBEJK
GUFHI
LHSKQ
RIE
RIL
ID FETCH-LOGICAL-i175t-d9e1003eb3eb7b51af866fa74b6d7e886411fc3c0a4a404584311c644ff59f7c3
IEDL.DBID RIE
ISBN 9781467360579
1467360570
ISICitedReferencesCount 11
ISICitedReferencesURI http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000423016400002&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
IngestDate Wed Sep 03 07:09:32 EDT 2025
IsPeerReviewed false
IsScholarly false
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-i175t-d9e1003eb3eb7b51af866fa74b6d7e886411fc3c0a4a404584311c644ff59f7c3
PageCount 7
ParticipantIDs ieee_primary_6511859
PublicationCentury 2000
PublicationDate 2012-Dec.
PublicationDateYYYYMMDD 2012-12-01
PublicationDate_xml – month: 12
  year: 2012
  text: 2012-Dec.
PublicationDecade 2010
PublicationTitle 2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology
PublicationTitleAbbrev wi-iat
PublicationYear 2012
Publisher IEEE
Publisher_xml – name: IEEE
SSID ssj0001035101
ssj0001120833
Score 1.5774837
Snippet As an effective technique for dimensionality reduction, feature selection has a broad application in different research areas. In this paper, we present a...
SourceID ieee
SourceType Publisher
StartPage 9
SubjectTerms Clustering algorithms
Computational modeling
Feature Clustering
Feature extraction
Feature Redundancy
Filters
High dimensional data
Laplace equations
Number of Features
Partitioning algorithms
Principal component analysis
Redundancy
Unsupervised Feature Selection
Vectors
Title Unsupervised Feature Selection with Feature Clustering
URI https://ieeexplore.ieee.org/document/6511859
Volume 1
WOSCitedRecordID wos000423016400002&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV09b8IwED0B6tCJtlD1Wxk61oATxx9jhYqKVCGkQsuGHMeWkKqAgPT312fStEOXbrGHKLrTxc_37t4B3BuUdRNGEZGqAWHUOiKVE4T7UJJMJ4YHKaW3FzGZyMVCTRvwUPfCWGtD8Znt4WPg8vO1KTFV1ucIh1PVhKYQ_NCr9ZNPQUqsolDDmsYeXSShlwtrlzwuqSWeqrWqpH7oQPXfx2T8OMNSr7gXo3Dpr1kr4agZtf_3kSfQ_enZi6b1aXQKDVucQft7aENUxXAH-LzYlRv8Q-xsHiECLLc2eg3jcLyPIkzM1tvDjxKFFPwLuzAfPc2Gz6QankBWHhHsSa4s9RHr78o2E1lKtZOcOy1YxnNhpeSMUmcSM9BMs8CWJpQaj46cS72zTHIOrWJd2AuINKWZscjn-btVptGCksXoSBk7lblL6KAZlpuDPsayssDV39vXcIxWPpSE3EBrvy3tLRyZz_1qt70LTv0Csh-cYg
linkProvider IEEE
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3PT8IwFH5BNNETKhp_u4NHC-vWde3REAlEJCSCciNb1yYkZhBg_v32lTk8ePG29rAs7-WtX9_33vcAHhTKusVKkjiSPmFUGyKkiQm3oSRYEirupJTeB_FwKKZTOarBY9ULo7V2xWe6hY-Oy88WqsBUWZsjHI7kHuxHjAX-tltrl1FBUqwkUd2aBhZfhK6bC6uXLDKpRJ7KtSzFfqgv2x990n8aY7FX0ApQuvTXtBV32HQb__vMYzjbde15o-o8OoGazk-h8TO2wSujuAl8kq-LJf4j1jrzEAMWK-29uYE41ksepmar7c5ngVIK9oVnMOk-jzs9Uo5PIHOLCTYkk5ramLW3ZZ3GaUQTIzg3ScxSnsVaCM4oNSpUfsIS5vjSkFJl8ZExkXWXCs-hni9yfQFeQmmqNDJ69naVJmhBwQJ0pQiMTM0lNNEMs-VWIWNWWuDq7-17OOyNXwezQX_4cg1HaPFtgcgN1DerQt_CgfrazNerO-fgbwuFn6k
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=2012+IEEE%2FWIC%2FACM+International+Conferences+on+Web+Intelligence+and+Intelligent+Agent+Technology&rft.atitle=Unsupervised+Feature+Selection+with+Feature+Clustering&rft.au=Cheung%2C+Yiu-Ming&rft.au=Jia%2C+Hong&rft.date=2012-12-01&rft.pub=IEEE&rft.isbn=9781467360579&rft.volume=1&rft.spage=9&rft.epage=15&rft_id=info:doi/10.1109%2FWI-IAT.2012.259&rft.externalDocID=6511859
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=9781467360579/lc.gif&client=summon&freeimage=true
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=9781467360579/mc.gif&client=summon&freeimage=true
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=9781467360579/sc.gif&client=summon&freeimage=true