Global and Fast Refinement of Greedy Sensor Selection Algorithms for Linear Models
This letter focuses on greedy approaches to select the most informative <inline-formula><tex-math notation="LaTeX">k</tex-math></inline-formula> sensors from <inline-formula><tex-math notation="LaTeX">N</tex-math></inline-formula> c...
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| Vydané v: | IEEE signal processing letters Ročník 32; s. 2574 - 2578 |
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| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
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2025
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| ISSN: | 1070-9908, 1558-2361 |
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| Abstract | This letter focuses on greedy approaches to select the most informative <inline-formula><tex-math notation="LaTeX">k</tex-math></inline-formula> sensors from <inline-formula><tex-math notation="LaTeX">N</tex-math></inline-formula> candidates to form a measurement submatrix that minimizes the estimation error. It is a submatrix selection problem. We refine conventional greedy sensor selection algorithms based on the square maximum-volume (SMV) submatrices finding method, particularly at their <inline-formula><tex-math notation="LaTeX">n</tex-math></inline-formula>th step, with <inline-formula><tex-math notation="LaTeX">n</tex-math></inline-formula> being the problem dimension. Our main idea is to increase the volume of the square measurement submatrix associated with the <inline-formula><tex-math notation="LaTeX">n</tex-math></inline-formula> sensors by iteratively swapping the selected and unselected sensors based on the dominant property of the maximum-volume submatrix. This simple refinement method ensures a square measurement matrix with increased volume, facilitating the subsequent greedy steps. It can be easily applied to existing greedy algorithms for performance improvement without increasing their complexity order. Numerical results demonstrate the effectiveness of the proposed refinement method in improving several popular greedy algorithms. |
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| AbstractList | This letter focuses on greedy approaches to select the most informative <inline-formula><tex-math notation="LaTeX">k</tex-math></inline-formula> sensors from <inline-formula><tex-math notation="LaTeX">N</tex-math></inline-formula> candidates to form a measurement submatrix that minimizes the estimation error. It is a submatrix selection problem. We refine conventional greedy sensor selection algorithms based on the square maximum-volume (SMV) submatrices finding method, particularly at their <inline-formula><tex-math notation="LaTeX">n</tex-math></inline-formula>th step, with <inline-formula><tex-math notation="LaTeX">n</tex-math></inline-formula> being the problem dimension. Our main idea is to increase the volume of the square measurement submatrix associated with the <inline-formula><tex-math notation="LaTeX">n</tex-math></inline-formula> sensors by iteratively swapping the selected and unselected sensors based on the dominant property of the maximum-volume submatrix. This simple refinement method ensures a square measurement matrix with increased volume, facilitating the subsequent greedy steps. It can be easily applied to existing greedy algorithms for performance improvement without increasing their complexity order. Numerical results demonstrate the effectiveness of the proposed refinement method in improving several popular greedy algorithms. |
| Author | Hua, Cunqing Wang, Yiyin Liu, Lingya |
| Author_xml | – sequence: 1 givenname: Lingya orcidid: 0000-0002-4877-5951 surname: Liu fullname: Liu, Lingya email: lyliu@shmtu.edu.cn organization: College of Information Engineering, Shanghai Maritime University, Shanghai, China – sequence: 2 givenname: Yiyin orcidid: 0000-0002-4464-6589 surname: Wang fullname: Wang, Yiyin email: yiyinwang@sjtu.edu.cn organization: State Key Laboratory of Submarine Geoscience, School of Automation and Intelligent Sensing, Shanghai Jiao Tong University, Shanghai, China – sequence: 3 givenname: Cunqing orcidid: 0000-0003-0243-805X surname: Hua fullname: Hua, Cunqing email: cqhua@sjtu.edu.cn organization: School of Computer Science, Shanghai Jiao Tong University, Shanghai, China |
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| Cites_doi | 10.1142/9789812836021_0015 10.1109/LSP.2017.2783350 10.1109/RadarConf2043947.2020.9266660 10.1109/TSP.2017.2773429 10.1109/TCOMM.2010.09.0901232 10.1109/TSP.2014.2299518 10.1016/j.applthermaleng.2015.09.078 10.1109/TSP.2008.2007095 10.1109/TSP.2014.2379662 10.1109/TSP.2019.2903017 10.1287/opre.2023.2488 10.1109/TSP.2016.2550005 10.1109/ICASSP49660.2025.10888657 10.1109/TSP.2023.3283047 10.1109/TSP.2013.2289881 10.1109/CDC.2010.5717225 10.1109/LSP.2013.2297419 10.1109/TSP.2016.2573767 10.1109/TSP.2003.821099 |
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| SubjectTerms | Atmospheric measurements Complexity theory Covariance matrices dominant submatrix Eigenvalues and eigenfunctions global search Greedy algorithms linear model maximum-volume submatrix Pollution measurement Sensor selection Signal processing algorithms Temperature measurement Training Volume measurement |
| Title | Global and Fast Refinement of Greedy Sensor Selection Algorithms for Linear Models |
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