Convergence analysis of a non-negative matrix factorization algorithm based on Gibbs random field modeling

Non-negative matrix factorization (NMF) is a new approach to deal with the multivariate nonnegative data. Although the classic multiplicative update algorithm can solve the NMF problems, it fails to find sparse and localized object parts. Then a Gibbs random field (GRF) modeling based NMF algorithm,...

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Veröffentlicht in:	Journal of applied mathematics & computing Jg. 42; H. 1-2; S. 491 - 508
Hauptverfasser:	Yang, Chenxue, Ye, Mao, Liu, Zijian
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer-Verlag 01.07.2013 Springer Nature B.V
Schlagworte:	Algorithms Analysis Approximation Computation Computational Mathematics and Numerical Analysis Computer science Construction Convergence Decomposition Equilibrium Factorization Invariants Mathematical and Computational Engineering Mathematical models Mathematics Mathematics and Statistics Mathematics of Computing Matrix Neighborhoods Original Research Stability Studies Theory of Computation 68Q25 68W40 Gibbs random field (GRF) Stability Non-negative matrix factorizations Existence Convergence Equilibrium point
ISSN:	1598-5865, 1865-2085
Online-Zugang:	Volltext
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