One-bit Matrix Compressed Sensing Algorithm for Sparse Matrix Recovery
Summary :
This paper studies the problem of recovering an arbitrarily distributed sparse matrix from its one-bit (1-bit) compressive measurements. We propose a matrix sketching based binary method iterative hard thresholding (MSBIHT) algorithm by combining the two dimensional version of BIHT (2DBIHT) and the matrix sketching method, to solve the sparse matrix recovery problem in matrix form. In contrast to traditional one-dimensional BIHT (BIHT), the proposed algorithm can reduce computational complexity. Besides, the MSBIHT can also improve the recovery performance comparing to the 2DBIHT method. A brief theoretical analysis and numerical experiments show the proposed algorithm outperforms traditional ones.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E99-A No.2 pp.647-650
- Publication Date
- 2016/02/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E99.A.647
- Type of Manuscript
- LETTER
- Category
- Digital Signal Processing
Authors
Hui WANG
UESTC
Sabine VAN HUFFEL
KU Leuven
Guan GUI
NUPT
Qun WAN
UESTC
Keyword
Latest Issue
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Hui WANG, Sabine VAN HUFFEL, Guan GUI, Qun WAN, "One-bit Matrix Compressed Sensing Algorithm for Sparse Matrix Recovery" in IEICE TRANSACTIONS on Fundamentals,
vol. E99-A, no. 2, pp. 647-650, February 2016, doi: 10.1587/transfun.E99.A.647.
Abstract: This paper studies the problem of recovering an arbitrarily distributed sparse matrix from its one-bit (1-bit) compressive measurements. We propose a matrix sketching based binary method iterative hard thresholding (MSBIHT) algorithm by combining the two dimensional version of BIHT (2DBIHT) and the matrix sketching method, to solve the sparse matrix recovery problem in matrix form. In contrast to traditional one-dimensional BIHT (BIHT), the proposed algorithm can reduce computational complexity. Besides, the MSBIHT can also improve the recovery performance comparing to the 2DBIHT method. A brief theoretical analysis and numerical experiments show the proposed algorithm outperforms traditional ones.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E99.A.647/_p
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@ARTICLE{e99-a_2_647,
author={Hui WANG, Sabine VAN HUFFEL, Guan GUI, Qun WAN, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={One-bit Matrix Compressed Sensing Algorithm for Sparse Matrix Recovery},
year={2016},
volume={E99-A},
number={2},
pages={647-650},
abstract={This paper studies the problem of recovering an arbitrarily distributed sparse matrix from its one-bit (1-bit) compressive measurements. We propose a matrix sketching based binary method iterative hard thresholding (MSBIHT) algorithm by combining the two dimensional version of BIHT (2DBIHT) and the matrix sketching method, to solve the sparse matrix recovery problem in matrix form. In contrast to traditional one-dimensional BIHT (BIHT), the proposed algorithm can reduce computational complexity. Besides, the MSBIHT can also improve the recovery performance comparing to the 2DBIHT method. A brief theoretical analysis and numerical experiments show the proposed algorithm outperforms traditional ones.},
keywords={},
doi={10.1587/transfun.E99.A.647},
ISSN={1745-1337},
month={February},}
Copy
TY - JOUR
TI - One-bit Matrix Compressed Sensing Algorithm for Sparse Matrix Recovery
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 647
EP - 650
AU - Hui WANG
AU - Sabine VAN HUFFEL
AU - Guan GUI
AU - Qun WAN
PY - 2016
DO - 10.1587/transfun.E99.A.647
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E99-A
IS - 2
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - February 2016
AB - This paper studies the problem of recovering an arbitrarily distributed sparse matrix from its one-bit (1-bit) compressive measurements. We propose a matrix sketching based binary method iterative hard thresholding (MSBIHT) algorithm by combining the two dimensional version of BIHT (2DBIHT) and the matrix sketching method, to solve the sparse matrix recovery problem in matrix form. In contrast to traditional one-dimensional BIHT (BIHT), the proposed algorithm can reduce computational complexity. Besides, the MSBIHT can also improve the recovery performance comparing to the 2DBIHT method. A brief theoretical analysis and numerical experiments show the proposed algorithm outperforms traditional ones.
ER -
English
