Online Nonnegative Matrix Factorization With Outliers

@article{Zhao2016OnlineNM,
  title={Online Nonnegative Matrix Factorization With Outliers},
  author={Renbo Zhao and Vincent Y. F. Tan},
  journal={IEEE Transactions on Signal Processing},
  year={2016},
  volume={65},
  pages={555-570}
}
We propose a unified and systematic framework for performing online nonnegative matrix factorization in the presence of outliers. Our framework is particularly suited to large-scale data. We propose two solvers based on projected gradient descent and the alternating direction method of multipliers. We prove that the sequence of objective values converges almost surely by appealing to the quasi-martingale convergence theorem. We also show the sequence of learned dictionaries converges to the set… CONTINUE READING
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