The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
An Interior-Point Method for Large-Scale -Regularized Least Squares
Abstract
Recently, a lot of attention has been paid to regularization based methods for sparse signal reconstruction (e.g., basis pursuit denoising and compressed sensing) and feature selection (e.g., the Lasso algorithm) in signal processing, statistics, and related fields. These problems can be cast as -regularized least-squares programs (LSPs), which can be reformulated as convex quadratic programs, and then solved by several standard methods such as interior-point methods, at least for small and medium size problems. In this paper, we describe a specialized interior-point method for solving large-scale -regularized LSPs that uses the preconditioned conjugate gradients algorithm to compute the search direction. The interior-point method can solve large sparse problems, with a million variables and observations, in a few tens of minutes on a PC. It can efficiently solve large dense problems, that arise in sparse signal recovery with orthogonal transforms, by exploiting fast algorithms for these transforms. The method is illustrated on a magnetic resonance imaging data set.
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Cites
Boyd, S., & Vandenberghe, L. (2004) · doi:10.1017/cbo9780511804441 · Cited by 56,601
Self-citationRegression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482
The Elements of Statistical Learning
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The adaptive lasso and its oracle properties
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Cited by
Boyd, S., Parikh, N., Chu, E., Peleato, B., & Eckstein, J. (2011) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000016 · Cited by 17,075
Self-citation(2009) · doi:10.1109/iccv.2009.5459292 · Cited by 779
Massive Access for 5G and Beyond
Chen, X., Ng, D. W. K., Yu, W., Larsson, E. G., Al-Dhahir, N., & Schober, R. (2021) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2020.3019724 · Cited by 681
Sparse coding based visual tracking: Review and experimental comparison
Zhang, S., Yao, H., Sun, X., & Lu, X. (2013) · Pattern Recognition · doi:10.1016/j.patcog.2012.10.006 · Cited by 284
Yang, Y., & Nagarajaiah, S. (2014) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2013.09.009 · Cited by 101
L 1 regularization approach to structural damage detection using frequency data
Zhou, X.-Q., Xia, Y., & Weng, S. (2015) · Structural Health Monitoring · doi:10.1177/1475921715604386 · Cited by 99
Hernandez, E. M. (2014) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2013.12.009 · Cited by 74
Emerging data technology in structural health monitoring: compressive sensing technology
Bao, Y., Li, H., & Ou, J. (2014) · Journal of Civil Structural Health Monitoring · doi:10.1007/s13349-013-0064-1 · Cited by 43
Sparsity-Aware Learning and Compressed Sensing: An Overview
Theodoridis, S., Kopsinis, Y., & Slavakis, K. (2014) · Academic Press Library in Signal Processing · doi:10.1016/b978-0-12-396502-8.00023-1 · Cited by 14
Metadata
- Authors
- Kim, S.-J., Koh, K., Lustig, M., Boyd, S., & Gorinevsky, D.
- Venue
- IEEE Journal of Selected Topics in Signal Processing
- Published
- 2007-12
- Volume
- 1
- Issue
- 4
- Pages
- 606-617
- ISSN
- 1932-4553 (ISSN-L)
- 1941-0484
- OpenAlex
- W3022380717
- Cited by
- 2,083
- References
- 4
- FWCI
- 82.25
- Type
- journal article
- Language
- en
- Open access
- closed
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