Robust and Scalable Column/Row Sampling from Corrupted Big Data

ICCVW 2017 pp. 1818-1826

doi:10.1109/ICCVW.2017.215 /iccvw/2017/rahmani2017iccvw-robust/

Abstract

Conventional sampling techniques fall short of drawing descriptive sketches of the data when the data is grossly corrupted as such corruptions break the low rank structure required for them to perform satisfactorily. In this paper, we present new sampling algorithms which can locate the informative columns in presence of severe data corruptions. In addition, we develop new scalable randomized designs of the proposed algorithms. The proposed approach is simultaneously robust to sparse corruption and outliers and substantially outperforms the state-of-the-art robust sampling algorithms as demonstrated by experiments conducted using both real and synthetic data.

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Cite

Text

Rahmani and Atia. "Robust and Scalable Column/Row Sampling from Corrupted Big Data." IEEE/CVF International Conference on Computer Vision Workshops, 2017. doi:10.1109/ICCVW.2017.215

Markdown

[Rahmani and Atia. "Robust and Scalable Column/Row Sampling from Corrupted Big Data." IEEE/CVF International Conference on Computer Vision Workshops, 2017.](https://mlanthology.org/iccvw/2017/rahmani2017iccvw-robust/) doi:10.1109/ICCVW.2017.215

BibTeX

@inproceedings{rahmani2017iccvw-robust,
  title     = {{Robust and Scalable Column/Row Sampling from Corrupted Big Data}},
  author    = {Rahmani, Mostafa and Atia, George K.},
  booktitle = {IEEE/CVF International Conference on Computer Vision Workshops},
  year      = {2017},
  pages     = {1818-1826},
  doi       = {10.1109/ICCVW.2017.215},
  url       = {https://mlanthology.org/iccvw/2017/rahmani2017iccvw-robust/}
}