Online nonnegative matrix factorization with outliers

@article{Zhao2016OnlineNM,
  title={Online nonnegative matrix factorization with outliers},
  author={Renbo Zhao and Vincent Yan Fu Tan},
  journal={2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  year={2016},
  pages={2662-2666}
}
  • Renbo Zhao, V. Tan
  • Published 10 April 2016
  • Mathematics, Computer Science
  • 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
We propose an optimization framework for performing online Non-negative Matrix Factorization (NMF) in the presence of outliers, based on l\ regularization and stochastic approximation. Due to the online nature of the algorithm, the proposed method has extremely low computational and storage complexity and is thus particularly applicable in this age of BigData. Furthermore, our algorithm shows promising performance in dealing with outliers, which previous online NMF algorithms fail to cope with… 
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References

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TLDR
The sequence of objective values converges almost surely by appealing to the quasi-martingale convergence theorem, and the sequence of learned dictionaries converges to the set of stationary points of the expected loss function almost surely.
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TLDR
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TLDR
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TLDR
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TLDR
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TLDR
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TLDR
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