Computing non-negative tensor factorizations.
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| Title: | Computing non-negative tensor factorizations. |
|---|---|
| Authors: | Friedlander, MichaelP.1 (AUTHOR) mpf@cs.ubc.ca, Hatz, Kathrin2 (AUTHOR) |
| Source: | Optimization Methods & Software. Aug2008, Vol. 23 Issue 4, p631-647. 17p. 1 Black and White Photograph, 1 Chart. |
| Subjects: | Least squares software, Sparse matrix software, Factorization, Mathematical optimization, Mathematical analysis |
| Abstract: | Non-negative tensor factorization (NTF) is a technique for computing a parts-based representation of high-dimensional data. NTF excels at exposing latent structures in datasets, and at finding good low-rank approximations to the data. We describe an approach for computing the NTF of a dataset that relies only on iterative linear-algebra techniques and that is comparable in cost to the non-negative matrix factorization (NMF). (The better-known NMF is a special case of NTF and is also handled by our implementation.) Some important features of our implementation include mechanisms for encouraging sparse factors and for ensuring that they are equilibrated in norm. The complete MATLAB software package is available under the GPL license. [ABSTRACT FROM AUTHOR] |
| Copyright of Optimization Methods & Software is the property of Taylor & Francis Ltd 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 |
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| Header | DbId: egs DbLabel: Engineering Source An: 33141069 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Computing non-negative tensor factorizations. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Friedlander%2C+MichaelP%2E%22">Friedlander, MichaelP.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> mpf@cs.ubc.ca</i><br /><searchLink fieldCode="AR" term="%22Hatz%2C+Kathrin%22">Hatz, Kathrin</searchLink><relatesTo>2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Optimization+Methods+%26+Software%22">Optimization Methods & Software</searchLink>. Aug2008, Vol. 23 Issue 4, p631-647. 17p. 1 Black and White Photograph, 1 Chart. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Least+squares+software%22">Least squares software</searchLink><br /><searchLink fieldCode="DE" term="%22Sparse+matrix+software%22">Sparse matrix software</searchLink><br /><searchLink fieldCode="DE" term="%22Factorization%22">Factorization</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+analysis%22">Mathematical analysis</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Non-negative tensor factorization (NTF) is a technique for computing a parts-based representation of high-dimensional data. NTF excels at exposing latent structures in datasets, and at finding good low-rank approximations to the data. We describe an approach for computing the NTF of a dataset that relies only on iterative linear-algebra techniques and that is comparable in cost to the non-negative matrix factorization (NMF). (The better-known NMF is a special case of NTF and is also handled by our implementation.) Some important features of our implementation include mechanisms for encouraging sparse factors and for ensuring that they are equilibrated in norm. The complete MATLAB software package is available under the GPL license. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Optimization Methods & Software is the property of Taylor & Francis Ltd 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.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/10556780801996244 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 17 StartPage: 631 Subjects: – SubjectFull: Least squares software Type: general – SubjectFull: Sparse matrix software Type: general – SubjectFull: Factorization Type: general – SubjectFull: Mathematical optimization Type: general – SubjectFull: Mathematical analysis Type: general Titles: – TitleFull: Computing non-negative tensor factorizations. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Friedlander, MichaelP. – PersonEntity: Name: NameFull: Hatz, Kathrin IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 08 Text: Aug2008 Type: published Y: 2008 Identifiers: – Type: issn-print Value: 10556788 Numbering: – Type: volume Value: 23 – Type: issue Value: 4 Titles: – TitleFull: Optimization Methods & Software Type: main |
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