Robust nonnegative matrix factorization via L1 norm regularization by multiplicative updating rules

@article{Shen2014RobustNM,
  title={Robust nonnegative matrix factorization via L1 norm regularization by multiplicative updating rules},
  author={Bin Shen and Luo Si and Rongrong Ji and Bao-Di Liu},
  journal={2014 IEEE International Conference on Image Processing (ICIP)},
  year={2014},
  pages={5282-5286}
}
Nonnegative Matrix Factorization (NMF) is a widely used technique in many applications such as face recognition, motion segmentation, etc. It approximates the nonnegative data in an original high dimensional space with a linear representation in a low dimensional space by using the product of two nonnegative matrices. In many applications data are often partially corrupted with large additive noise. When the positions of noise are known, some existing variants of N-MF can be applied by treating… CONTINUE READING
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