Non-negative Matrix Factorization under Heavy Noise

@inproceedings{Bhattacharyya2016NonnegativeMF,
  title={Non-negative Matrix Factorization under Heavy Noise},
  author={Chiranjib Bhattacharyya and Navin Goyal and Ravi Kannan and Jagdeep Pani},
  booktitle={ICML},
  year={2016}
}
The Noisy Non-negative Matrix factorization (NMF) is: given a data matrix A (d × n), find non-negative matrices B,C (d × k, k × n respy.) so that A = BC + N , where N is a noise matrix. Existing polynomial time algorithms with proven error guarantees require each column N·,j to have l1 norm much smaller than ||(BC)·,j ||1, which could be very restrictive. In important applications of NMF such as Topic Modeling as well as theoretical noise models (eg. Gaussian with high σ), almost every column… CONTINUE READING
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