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Proceedings Paper

Selective erasures for high-dimensional robust subspace tracking
Author(s): Daniel L. Pimentel-Alarcon
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Paper Abstract

This paper presents an online method to track a subspace U from severely corrupted and incomplete data. If we could identify the corrupted entries in a new observation x, then we would be able to update U according to the uncorrupted entries in x using an incomplete-data rank-one update. The challenge is to identify the corrupted entries in x, which is in general NP-hard. To work around this we propose an approach that iteratively removes the entries that most affect partial projections of x onto U. Our experiments show that this simple approach outperforms state-of-the-art methods, including ℓ1-optimization, specially when most entries in x are corrupted.

Paper Details

Date Published: 14 May 2018
PDF: 11 pages
Proc. SPIE 10658, Compressive Sensing VII: From Diverse Modalities to Big Data Analytics, 1065808 (14 May 2018); doi: 10.1117/12.2311891
Show Author Affiliations
Daniel L. Pimentel-Alarcon, Georgia State Univ. (United States)


Published in SPIE Proceedings Vol. 10658:
Compressive Sensing VII: From Diverse Modalities to Big Data Analytics
Fauzia Ahmad, Editor(s)

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