  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.

Machine Learning;Numerical Analysis;Applications;Machine Learning