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231223s2010 xx |||||o 00| ||eng c |
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|a 10.1109/TPAMI.2008.277
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|a eng
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| 100 |
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|a Ding, Chris
|e verfasserin
|4 aut
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| 245 |
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|a Convex and semi-nonnegative matrix factorizations
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|c 2010
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
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|2 rdamedia
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|a ƒa Online-Ressource
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|a Date Completed 25.01.2010
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|a Date Revised 20.11.2009
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|a published: Print
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|a Citation Status PubMed-not-MEDLINE
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|a We present several new variations on the theme of nonnegative matrix factorization (NMF). Considering factorizations of the form X=FG(T), we focus on algorithms in which G is restricted to containing nonnegative entries, but allowing the data matrix X to have mixed signs, thus extending the applicable range of NMF methods. We also consider algorithms in which the basis vectors of F are constrained to be convex combinations of the data points. This is used for a kernel extension of NMF. We provide algorithms for computing these new factorizations and we provide supporting theoretical analysis. We also analyze the relationships between our algorithms and clustering algorithms, and consider the implications for sparseness of solutions. Finally, we present experimental results that explore the properties of these new methods
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|a Journal Article
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|a Research Support, U.S. Gov't, Non-P.H.S.
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| 700 |
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|a Li, Tao
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Jordan, Michael I
|e verfasserin
|4 aut
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| 773 |
0 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 32(2010), 1 vom: 20. Jan., Seite 45-55
|w (DE-627)NLM098212257
|x 1939-3539
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|g volume:32
|g year:2010
|g number:1
|g day:20
|g month:01
|g pages:45-55
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|u http://dx.doi.org/10.1109/TPAMI.2008.277
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