Spectral Feature Selection for Data Mining

Zhao, Zheng Alan; Liu, Huan

doi:10.1201/b11426

dc.contributor.author	Zhao, Zheng Alan
dc.contributor.author	Liu, Huan
dc.date.accessioned	2025-11-25T08:01:55Z
dc.date.available	2025-11-25T08:01:55Z
dc.date.issued	2011
dc.date.submitted	2025-04-22T11:38:46Z
dc.identifier	ONIX_20250422_9781439862100_21
dc.identifier	ONIX_20250422_9781439862100_21a
dc.identifier	https://library.oapen.org/handle/20.500.12657/101027
dc.identifier.uri	https://doab-dev.siscern.org/handle/20.500.12854/205403
dc.description.abstract	Spectral Feature Selection for Data Mining introduces a novel feature selection technique that establishes a general platform for studying existing feature selection algorithms and developing new algorithms for emerging problems in real-world applications. This technique represents a unified framework for supervised, unsupervised, and semisupervise
dc.language	English
dc.relation.ispartofseries	Chapman & Hall/CRC Data Mining and Knowledge Discovery Series
dc.rights	open access
dc.subject.classification	thema EDItEUR::U Computing and Information Technology::UY Computer science
dc.subject.classification	thema EDItEUR::U Computing and Information Technology::UN Databases::UNF Data mining
dc.subject.classification	thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TJ Electronics and communications engineering::TJF Electronics engineering::TJFM Automatic control engineering
dc.subject.classification	thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics
dc.subject.other	Feature Selection Algorithms
dc.subject.other	Feature Selection
dc.subject.other	Spectral Feature Selection
dc.subject.other	Multivariate Formulations
dc.subject.other	data mining
dc.subject.other	Data Set
dc.subject.other	machine learning
dc.subject.other	Fisher Score
dc.subject.other	dimensionality reduction
dc.subject.other	Laplacian Matrix
dc.subject.other	Similarity Matrix
dc.subject.other	feature extraction
dc.subject.other	high-dimensional data processing
dc.subject.other	Redundant Features
dc.subject.other	Existing Feature Selection
dc.subject.other	Normalized Laplacian Matrix
dc.subject.other	Rank Aggregation
dc.subject.other	F2 F3 F4 F5 F6
dc.subject.other	Feature Selection Techniques
dc.subject.other	F1 F2 F3 F4 F5
dc.subject.other	TIMP Metallopeptidase Inhibitor
dc.subject.other	Gene Selection
dc.subject.other	Computer Nodes
dc.subject.other	microRNA Microarray
dc.subject.other	LDA
dc.title	Spectral Feature Selection for Data Mining
dc.type	book
oapen.identifier.doi	10.1201/b11426
oapen.relation.isPublishedBy	fa69b019-f4ee-4979-8d42-c6b6c476b5f0
oapen.relation.isFundedBy	969f21b5-ac00-4517-9de2-44973eec6874
oapen.relation.isFundedBy	b818ba9d-2dd9-4fd7-a364-7f305aef7ee9
oapen.relation.isbn	9781439862100
oapen.relation.isbn	9781138112629
oapen.relation.isbn	9781439862094
oapen.relation.isbn	9781000023046
oapen.relation.isbn	9780429107191
oapen.relation.isbn	9781000023077
oapen.collection	Knowledge Unlatched (KU)
oapen.collection	KU Select 2018: STEM Backlist Books
oapen.imprint	Chapman and Hall/CRC
oapen.pages	224
oapen.grant.number	[...]
peerreview.review.type	Proposal
peerreview.anonymity	Single-anonymised
peerreview.reviewer.type	Internal editor
peerreview.reviewer.type	External peer reviewer
peerreview.review.stage	Pre-publication
peerreview.open.review	No
peerreview.publish.responsibility	Publisher
peerreview.id	bc80075c-96cc-4740-a9f3-a234bc2598f1
dc.relationisFundedBy	b818ba9d-2dd9-4fd7-a364-7f305aef7ee9
peerreview.title	Proposal review

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