Robust And Scalable Column/Row Sampling From Corrupted Big Data

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.

Publication Date

7-1-2017

Publication Title

Proceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017

Volume

2018-January

Number of Pages

1818-1826

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/ICCVW.2017.215

Socpus ID

85046294433 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85046294433

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