ML Anthology

Streaming Principal Component Analysis in Noisy Setting

Teodor Vanislavov Marinov, Poorya Mianjy, Raman Arora
ICML 2018 pp. 3413-3422
/icml/2018/marinov2018icml-streaming/

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.

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Cite

Text

Marinov et al. "Streaming Principal Component Analysis in Noisy Setting." International Conference on Machine Learning, 2018.

Markdown

[Marinov et al. "Streaming Principal Component Analysis in Noisy Setting." International Conference on Machine Learning, 2018.](https://mlanthology.org/icml/2018/marinov2018icml-streaming/)

BibTeX

@inproceedings{marinov2018icml-streaming,
  title     = {{Streaming Principal Component Analysis in Noisy Setting}},
  author    = {Marinov, Teodor Vanislavov and Mianjy, Poorya and Arora, Raman},
  booktitle = {International Conference on Machine Learning},
  year      = {2018},
  pages     = {3413-3422},
  volume    = {80},
  url       = {https://mlanthology.org/icml/2018/marinov2018icml-streaming/}
}