Streaming Principal Component Analysis in Noisy Setting

@InProceedings{pmlr-v80-marinov18a,
  title = 	 {Streaming Principal Component Analysis in Noisy Setting},
  author =       {Marinov, Teodor Vanislavov and Mianjy, Poorya and Arora, Raman},
  booktitle = 	 {Proceedings of the 35th International Conference on Machine Learning},
  pages = 	 {3413--3422},
  year = 	 {2018},
  editor = 	 {Dy, Jennifer and Krause, Andreas},
  volume = 	 {80},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {10--15 Jul},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v80/marinov18a/marinov18a.pdf},
  url = 	 {https://proceedings.mlr.press/v80/marinov18a.html},
  abstract = 	 {We study streaming algorithms for principal component analysis (PCA) in noisy settings. We present computationally efficient algorithms with sub-linear regret bounds for PCA in the presence of noise, missing data, and gross outliers.}
}