Online Nonnegative Matrix Factorization With Robust Stochastic Approximation

Nonnegative matrix factorization (NMF) has become a popular dimension-reduction method and has been widely applied to image processing and pattern recognition problems. However, conventional NMF learning methods require the entire dataset to reside in the memory and thus cannot be applied to large-s...

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Veröffentlicht in:	IEEE transaction on neural networks and learning systems 2012-07, Vol.23 (7), p.1087-1099
Hauptverfasser:	Guan, Naiyang, Tao, Dacheng, Luo, Zhigang, Yuan, Bo
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithm design and analysis Algorithmics. Computability. Computer arithmetics Algorithms Applied sciences Approximation algorithms Approximation methods Artificial intelligence Complexity theory Computer science control theory systems Computer systems and distributed systems. User interface Convergence Exact sciences and technology Factorization Learning Mathematical analysis Nonnegative matrix factorization (NMF) On-line systems Online online NMF (ONMF) Pattern recognition. Digital image processing. Computational geometry robust stochastic approximation Robustness Software Stochasticity Streaming media Studies Theoretical computing
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