Incremental Nonnegative Matrix Factorization for Face Recognition

@inproceedings{Chen2008IncrementalNM,
  title={Incremental Nonnegative Matrix Factorization for Face Recognition},
  author={Wen-Sheng Chen and Binbin Pan and Bin Fang and Ming Li and Jianliang Tang},
  year={2008}
}
Nonnegative matrix factorization (NMF) is a promising approach for local feature extraction in face recognition tasks. However, there are two major drawbacks in almost all existing NMF-based methods. One shortcoming is that the computational cost is expensive for large matrix decomposition. The other is that it must conduct repetitive learning, when the training samples or classes are updated. To overcome these two limitations, this paper proposes a novel incremental nonnegative matrix… CONTINUE READING

Citations

Publications citing this paper.
SHOWING 1-10 OF 22 CITATIONS

A Novel Enhanced Nonnegative Feature Extraction Approach

  • 2018 14th International Conference on Computational Intelligence and Security (CIS)
  • 2018
VIEW 1 EXCERPT
CITES METHODS

References

Publications referenced by this paper.
SHOWING 1-10 OF 18 REFERENCES

On the convergence of multiplicative update algorithms for nonnegative matrix factorization,

C.-J. Lin
  • IEEE Transactions on Neural Networks,
  • 2007

Nonsmooth nonnegative matrix factorization  nsNMF 

S. Zafeiriou Kotsia, I. Pitas
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • 2006

A new sparse image representation algorithm applied to facial expression recognition

  • Proceedings of the 2004 14th IEEE Signal Processing Society Workshop Machine Learning for Signal Processing, 2004.
  • 2004