Scalable And Robust Pca Approach With Random Column/Row Sampling

Keywords

Big Data; Data Sketching; Low Rank Matrix; Outlier; Randomized Method; Robust PCA; Subspace Recovery

Abstract

This paper develops and analyzes a randomized design for robust Principal Component Analysis (PCA). In the proposed randomized method, a data sketch is constructed using random row sampling followed by random column sampling. The proposed randomized approach is shown to bring about substantial savings in complexity and memory requirements for robust subspace learning over conventional approaches that use the full scale data. A characterization of the sample and computational complexity for the randomized approach is derived. It is shown that the correct subspace can be recovered with computational and sample complexity that are almost independent of the size of the data. The results of the mathematical analysis are confirmed through numerical simulations using both synthetic and real data.

Publication Date

4-19-2017

Publication Title

2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings

Number of Pages

1320-1324

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/GlobalSIP.2016.7906055

Socpus ID

85019204803 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85019204803

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