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dc.contributor.authorZhao, Zheng Alan
dc.contributor.authorLiu, Huan
dc.date.accessioned2019-04-11 23:55
dc.date.accessioned2020-03-17 03:00:34
dc.date.accessioned2020-04-01T10:32:49Z
dc.date.available2020-04-01T10:32:49Z
dc.date.issued2012-01-01
dc.identifier1004820
dc.identifierOCN: 773311146en_US
dc.identifier.urihttp://library.oapen.org/handle/20.500.12657/25274
dc.description.abstractThis timely introduction to spectral feature selection illustrates the potential of this powerful dimensionality reduction technique in high-dimensional data processing. It presents the theoretical foundations of spectral feature selection, its connections to other algorithms, and its use in handling both large-scale data sets and small sample problems. Readers learn how to use spectral feature selection to solve challenging problems in real-life applications and discover how general feature selection and extraction are connected to spectral feature selection. Source code for the algorithms is available online.
dc.languageEnglish
dc.subject.classificationthema EDItEUR::U Computing and Information Technology::UN Databases::UNF Data miningen_US
dc.subject.otherComputer Science
dc.titleSpectral Feature Selection for Data Mining
dc.typebook
oapen.identifier.doi10.1201/b11426
oapen.relation.isPublishedBy7b3c7b10-5b1e-40b3-860e-c6dd5197f0bb
oapen.relation.isFundedByb818ba9d-2dd9-4fd7-a364-7f305aef7ee9
oapen.relation.isbn9781439862094
oapen.collectionKnowledge Unlatched (KU)
oapen.grant.number102700
oapen.grant.programKU Select 2018: STEM Backlist Books
oapen.identifier.isbn9781439862094
grantor.number102700
oapen.identifier.ocn773311146
peerreview.anonymitySingle-anonymised
peerreview.idbc80075c-96cc-4740-a9f3-a234bc2598f1
peerreview.open.reviewNo
peerreview.publish.responsibilityPublisher
peerreview.review.stagePre-publication
peerreview.review.typeProposal
peerreview.reviewer.typeInternal editor
peerreview.reviewer.typeExternal peer reviewer
peerreview.titleProposal review
oapen.review.commentsTaylor & Francis open access titles are reviewed as a minimum at proposal stage by at least two external peer reviewers and an internal editor (additional reviews may be sought and additional content reviewed as required).


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