Proceedings Paper
Compressive sensing solutions through minimax optimization| Format | Member Price | Non-Member Price |
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Paper Abstract
This paper is concerned with the basic issue of the robustness of compressive sensing solutions in the presence of uncertainties. In particular, we are interested in robust compressive sensing solutions under unknown modeling and measurement inaccuracies. The problems are formulated as minimax optimization. Exact solutions are derived through the approach of Alternating Direction Method of Multipliers. Numerical examples show the minimax problem formulations indeed improve the robustness of compressive sensing solutions in the presence of model and measurement uncertainties.
Paper Details
Date Published: 20 May 2015
PDF: 8 pages
Proc. SPIE 9496, Independent Component Analyses, Compressive Sampling, Large Data Analyses (LDA), Neural Networks, Biosystems, and Nanoengineering XIII, 94960E (20 May 2015); doi: 10.1117/12.2183914
Published in SPIE Proceedings Vol. 9496:
Independent Component Analyses, Compressive Sampling, Large Data Analyses (LDA), Neural Networks, Biosystems, and Nanoengineering XIII
Harold H. Szu; Liyi Dai; Yufeng Zheng, Editor(s)
PDF: 8 pages
Proc. SPIE 9496, Independent Component Analyses, Compressive Sampling, Large Data Analyses (LDA), Neural Networks, Biosystems, and Nanoengineering XIII, 94960E (20 May 2015); doi: 10.1117/12.2183914
Show Author Affiliations
Liyi Dai, U.S. Army Research Office (United States)
Published in SPIE Proceedings Vol. 9496:
Independent Component Analyses, Compressive Sampling, Large Data Analyses (LDA), Neural Networks, Biosystems, and Nanoengineering XIII
Harold H. Szu; Liyi Dai; Yufeng Zheng, Editor(s)
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