Abstract
In this paper, we examine the problem of locating vector outliers from a large number of inliers, with a particular focus on the case where the outliers are represented in a known basis or dictionary. Using a convex demixing formulation, we provide provable guarantees for exact recovery of the space spanned by the inliers and the supports of the outlier columns, even when the rank of inliers is high and the number of outliers is a constant proportion of total observations. Comprehensive numerical experiments on both synthetic and hyper-spectral imaging real datasets demonstrate the efficiency of our proposed method.
Original language | English (US) |
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Title of host publication | 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 4699-4703 |
Number of pages | 5 |
ISBN (Print) | 9781538646588 |
DOIs | |
State | Published - Sep 10 2018 |
Event | 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Calgary, Canada Duration: Apr 15 2018 → Apr 20 2018 |
Publication series
Name | ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings |
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Volume | 2018-April |
ISSN (Print) | 1520-6149 |
Other
Other | 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 |
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Country/Territory | Canada |
City | Calgary |
Period | 4/15/18 → 4/20/18 |
Bibliographical note
Funding Information:Acknowledgment. The authors acknowledge support from the DARPA Young Faculty Award, Grant N66001-14-1-4047.
Publisher Copyright:
© 2018 IEEE.
Keywords
- Hyperspectral imaging
- Outlier identification
- Robust PCA