Online Nonnegative Matrix Factorization With Robust Stochastic Approximation

  title={Online Nonnegative Matrix Factorization With Robust Stochastic Approximation},
  author={Naiyang Guan and Dacheng Tao and Zhigang Luo and Bo Yuan},
  journal={IEEE Transactions on Neural Networks and Learning Systems},
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-scale or streaming datasets. In this paper, we propose an efficient online RSA-NMF algorithm (OR-NMF) that learns NMF in an incremental fashion and thus solves this problem. In particular, OR-NMF… CONTINUE READING
Highly Influential
This paper has highly influenced 14 other papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 367 citations. REVIEW CITATIONS
150 Citations
46 References
Similar Papers


Publications citing this paper.
Showing 1-10 of 150 extracted citations

368 Citations

Citations per Year
Semantic Scholar estimates that this publication has 368 citations based on the available data.

See our FAQ for additional information.


Publications referenced by this paper.
Showing 1-10 of 46 references

The IAPR benchmark: A new evaluation resource for visual information systems

  • M. Grubinger, P. D. Clough, M. Henning, D. Thomas
  • Proc. Int. Conf. Lang. Resour. Evaluat., Genoa…
  • 2006
Highly Influential
3 Excerpts

Similar Papers

Loading similar papers…