A Bias Trick for Centered Robust Principal Component Analysis (Student Abstract)

AAAI 2020 pp. 13807-13808

doi:10.1609/AAAI.V34I10.7175 /aaai/2020/he2020aaai-bias/

Abstract

Outlier based Robust Principal Component Analysis (RPCA) requires centering of the non-outliers. We show a “bias trick” that automatically centers these non-outliers. Using this bias trick we obtain the first RPCA algorithm that is optimal with respect to centering.

PDF AAAI Semantic Scholar

Cite

Text

He et al. "A Bias Trick for Centered Robust Principal Component Analysis (Student Abstract)." AAAI Conference on Artificial Intelligence, 2020. doi:10.1609/AAAI.V34I10.7175

Markdown

[He et al. "A Bias Trick for Centered Robust Principal Component Analysis (Student Abstract)." AAAI Conference on Artificial Intelligence, 2020.](https://mlanthology.org/aaai/2020/he2020aaai-bias/) doi:10.1609/AAAI.V34I10.7175

BibTeX

@inproceedings{he2020aaai-bias,
  title     = {{A Bias Trick for Centered Robust Principal Component Analysis (Student Abstract)}},
  author    = {He, Baokun and Wan, Guihong and Schweitzer, Haim},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2020},
  pages     = {13807-13808},
  doi       = {10.1609/AAAI.V34I10.7175},
  url       = {https://mlanthology.org/aaai/2020/he2020aaai-bias/}
}