Dependence maps, a dimensionality reduction with dependence distance for high-dimensional data.
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| Title: | Dependence maps, a dimensionality reduction with dependence distance for high-dimensional data. |
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| Authors: | Lee, Kichun1 skylee@hanyang.ac.kr, Gray, Alexander2 agray@cc.gatech.edu, Kim, Heeyoung3 hykim@gatech.edu |
| Source: | Data Mining & Knowledge Discovery. May2013, Vol. 26 Issue 3, p512-532. 21p. 5 Diagrams, 6 Graphs. |
| Subjects: | Statistical maps, Electronic data processing, Dimension reduction (Statistics), Dependence (Statistics), Markov processes, Data analysis |
| Abstract: | We introduce the dependence distance, a new notion of the intrinsic distance between points, derived as a pointwise extension of statistical dependence measures between variables. We then introduce a dimension reduction procedure for preserving this distance, which we call the dependence map. We explore its theoretical justification, connection to other methods, and empirical behavior on real data sets. [ABSTRACT FROM AUTHOR] |
| Copyright of Data Mining & Knowledge Discovery is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 85013610 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Dependence maps, a dimensionality reduction with dependence distance for high-dimensional data. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Lee%2C+Kichun%22">Lee, Kichun</searchLink><relatesTo>1</relatesTo><i> skylee@hanyang.ac.kr</i><br /><searchLink fieldCode="AR" term="%22Gray%2C+Alexander%22">Gray, Alexander</searchLink><relatesTo>2</relatesTo><i> agray@cc.gatech.edu</i><br /><searchLink fieldCode="AR" term="%22Kim%2C+Heeyoung%22">Kim, Heeyoung</searchLink><relatesTo>3</relatesTo><i> hykim@gatech.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Data+Mining+%26+Knowledge+Discovery%22">Data Mining & Knowledge Discovery</searchLink>. May2013, Vol. 26 Issue 3, p512-532. 21p. 5 Diagrams, 6 Graphs. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Statistical+maps%22">Statistical maps</searchLink><br /><searchLink fieldCode="DE" term="%22Electronic+data+processing%22">Electronic data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Dimension+reduction+%28Statistics%29%22">Dimension reduction (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Dependence+%28Statistics%29%22">Dependence (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Markov+processes%22">Markov processes</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analysis%22">Data analysis</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: We introduce the dependence distance, a new notion of the intrinsic distance between points, derived as a pointwise extension of statistical dependence measures between variables. We then introduce a dimension reduction procedure for preserving this distance, which we call the dependence map. We explore its theoretical justification, connection to other methods, and empirical behavior on real data sets. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Data Mining & Knowledge Discovery is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=85013610 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10618-012-0267-9 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 21 StartPage: 512 Subjects: – SubjectFull: Statistical maps Type: general – SubjectFull: Electronic data processing Type: general – SubjectFull: Dimension reduction (Statistics) Type: general – SubjectFull: Dependence (Statistics) Type: general – SubjectFull: Markov processes Type: general – SubjectFull: Data analysis Type: general Titles: – TitleFull: Dependence maps, a dimensionality reduction with dependence distance for high-dimensional data. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Lee, Kichun – PersonEntity: Name: NameFull: Gray, Alexander – PersonEntity: Name: NameFull: Kim, Heeyoung IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2013 Type: published Y: 2013 Identifiers: – Type: issn-print Value: 13845810 Numbering: – Type: volume Value: 26 – Type: issue Value: 3 Titles: – TitleFull: Data Mining & Knowledge Discovery Type: main |
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