Robust and Scalable Column/Row Sampling from Corrupted Big Data
This addresses the challenge of data corruption in big data analysis for applications requiring reliable sampling, though it appears incremental as it builds on existing robust sampling methods.
The paper tackles the problem of sampling informative columns from grossly corrupted big data, where conventional methods fail, and demonstrates that the proposed robust and scalable algorithms substantially outperform state-of-the-art robust sampling techniques in experiments.
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.