Robust Pca With Concurrent Column And Element-Wise Outliers

Keywords

Big Data; Low Rank Matrix; Matrix Decomposition; Outlier Detection; Robust PCA; Sparse Approximation; Sparse Matrix; Sparse Representation

Abstract

To date, existing robust PCA algorithms have only considered settings where the data is corrupted with one type of outliers at a time, but a mechanism to handle simultaneous types of outliers has been lacking. This paper proposes a low rank matrix recovery algorithm that is robust to concurrent presence of column-wise and sparse element-wise outliers. The underpinning of our approach is a sparse approximation of a sparsely corrupted column whereby we set apart an inlier column with sparse corruption from an outlying data point. The core idea of sparse approximation is analyzed analytically where we show that the underlying-norm minimization can obtain the representation of an inlier in presence of sparse corruptions.

Publication Date

7-1-2017

Publication Title

55th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2017

Volume

2018-January

Number of Pages

332-337

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/ALLERTON.2017.8262756

Socpus ID

85047940406 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85047940406

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