Presentation 2011-06-30
A Modified Multiplicative Update Algorithm for Nonnegative Matrix Factorization and its Global Convergence : The Case of Euclidean Distance Minimization
Ryota HIBI, Norikazu TAKAHASHI,
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Abstract(in English) Nonnegative matrix factorization (NMF) is to approximate a given large nonnegative matrix by the product of two small nonnegative matrices. Although the multiplicative update algorithm is widely used as an efficient technique for NMF, it has a serious drawback that the update formulas are not well-defined because they are expressed in the form of a fraction. Furthermore, due to this drawback, the global convergence of the algorithm has not been guaranteed. In this report, we consider NMF in which the approximation error is measured by the Euclidean distance between matrices. We propose a modified multiplicative update algorithm in order to overcome the drawback of the original version and prove its global convergence.
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Keyword(in English) nonnegative matrix factorization / multiplicative update / Euclidean distance / global convergence
Paper # NLP2011-32
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Committee NLP
Conference Date 2011/6/23(1days)
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Registration To Nonlinear Problems (NLP)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) A Modified Multiplicative Update Algorithm for Nonnegative Matrix Factorization and its Global Convergence : The Case of Euclidean Distance Minimization
Sub Title (in English)
Keyword(1) nonnegative matrix factorization
Keyword(2) multiplicative update
Keyword(3) Euclidean distance
Keyword(4) global convergence
1st Author's Name Ryota HIBI
1st Author's Affiliation Graduate School of Information Science and Electrical Engineering, Kyushu University()
2nd Author's Name Norikazu TAKAHASHI
2nd Author's Affiliation Faculty of Information Science and Electrical Engineering, Kyushu University
Date 2011-06-30
Paper # NLP2011-32
Volume (vol) vol.111
Number (no) 106
Page pp.pp.-
#Pages 6
Date of Issue