Robust non-negative matrix factorization

  title={Robust non-negative matrix factorization},
  author={Lijun Zhang and Zhengguang Chen and Miao Zheng and He Xiaofei},
Non-negative matrix factorization (NMF) is a recently popularized technique for learning partsbased, linear representations of non-negative data. The traditional NMF is optimized under the Gaussian noise or Poisson noise assumption, and hence not suitable if the data are grossly corrupted. To improve the robustness of NMF, a novel algorithm named robust nonnegative matrix factorization (RNMF) is proposed in this paper. We assume that some entries of the data matrix may be arbitrarily corrupted… CONTINUE READING
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