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Article: A General Framework for Consistency of principal component analysis

TitleA General Framework for Consistency of principal component analysis
Authors
Shen, DShen, HMarron, J
KeywordsHigh dimension low sample size
PCA
Random matrix
Spike model
Issue Date2016
PublisherJournal of Machine Learning Research. The Journal's web site is located at http://mitpress.mit.edu/jmlr
Citation
Journal of Machine Learning Research, 2016, v. 17, p. 1-34 How to Cite?
Persistent Identifierhttp://hdl.handle.net/10722/248621
ISSN
1532-4435
2023 Impact Factor: 4.3
2023 SCImago Journal Rankings: 2.796

 

DC FieldValueLanguage
dc.contributor.authorShen, D-
dc.contributor.authorShen, H-
dc.contributor.authorMarron, J-
dc.date.accessioned2017-10-18T08:46:02Z-
dc.date.available2017-10-18T08:46:02Z-
dc.date.issued2016-
dc.identifier.citationJournal of Machine Learning Research, 2016, v. 17, p. 1-34-
dc.identifier.issn1532-4435-
dc.identifier.urihttp://hdl.handle.net/10722/248621-
dc.languageeng-
dc.publisherJournal of Machine Learning Research. The Journal's web site is located at http://mitpress.mit.edu/jmlr-
dc.relation.ispartofJournal of Machine Learning Research-
dc.subjectHigh dimension low sample size-
dc.subjectPCA-
dc.subjectRandom matrix-
dc.subjectSpike model-
dc.titleA General Framework for Consistency of principal component analysis-
dc.typeArticle-
dc.identifier.emailShen, H: haipeng@hku.hk-
dc.identifier.authorityShen, H=rp02082-
dc.description.naturepublished_or_final_version-
dc.identifier.scopuseid_2-s2.0-84995511095-
dc.identifier.hkuros279499-
dc.identifier.volume17-
dc.identifier.spage1-
dc.identifier.epage34-
dc.publisher.placeUnited States-
dc.identifier.issnl1532-4435-

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