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Solving Least Squares Problems
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“To mitigate this bias, one strategy is to weight current sources by the norm of the EEG/MEG signals that can be generated by each of them with a unitary magnitude. Introducing this weighting to MN regularization leads to the so-called weighted minimum norm (WMN) solution (24–27).”
From Electrophysiological source imaging: A noninvasive window to brain dynamics (2018) · cites this paper as [25]
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Items 1,001–1,008 of 1,008
Estimating multi-year 24 / 7 origin-destination demand using high-granular multi-source traffic data
Ma, W., & Qian, Z. (. (2018) · Transportation Research Part C: Emerging Technologies · doi:10.1016/j.trc.2018.09.002 · Cited by 56
Hyperspectral unmixing for Raman spectroscopy via physics-constrained autoencoders
Georgiev, D., Fernández-Galiana, Á., Vilms Pedersen, S., Papadopoulos, G., Xie, R., Stevens, M. M., & Barahona, M. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2407439121 · Cited by 50
Cellular Anatomy of the Mouse Primary Motor Cortex
Muñoz-Castañeda, R., Zingg, B., Matho, K. S., Wang, Q., Chen, X., Foster, N. N., Narasimhan, A., Li, A., Hirokawa, K. E., Huo, B., et al. (2020) · doi:10.1101/2020.10.02.323154 · Cited by 270
Kolind, S. H., Laule, C., Vavasour, I. M., Li, D. K. B., Traboulsee, A. L., Mädler, B., Moore, G. R. W., & MacKay, A. L. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2007.11.033 · Cited by 44
An inverse problem approach to optical homodyne tomography
Tan, S. M. (1997) · Journal of Modern Optics · doi:10.1080/09500349708231881 · Cited by 40
Electrophysiological Mapping and Neuroimaging
He, B., & Ding, L. (2013) · Neural Engineering · doi:10.1007/978-1-4614-5227-0_12 · Cited by 26
Brookes, E., Rocco, M., Vachette, P., & Trewhella, J. (2023) · Journal of Applied Crystallography · doi:10.1107/s1600576723005344 · Cited by 21
Red Sea deep water circulation and ventilation rate deduced from the 3He and 14C tracer fields
Jean-Baptiste, P., Fourré, E., Metzl, N., Ternon, J. F., & Poisson, A. (2004) · Journal of Marine Systems · doi:10.1016/j.jmarsys.2003.07.001 · Cited by 16
Metadata
- Authors
- Lawson, C. L., & Hanson, R. J.
- Affiliations
- California Institute of Technology (US)
- Venue
- Society for Industrial and Applied Mathematics eBooks
- Published
- 1995-01-01
- OpenAlex
- W2075665712
- Cited by
- 7,103
- References
- 0
- Citations by year
- 2026: 111 · 2025: 129 · 2024: 154 · 2023: 165 · 2022: 183 · 2021: 224 · 2020: 217 · 2019: 237 · 2018: 250 · 2017: 216 · 2016: 210 · 2015: 241 · 2014: 270 · 2013: 265 · 2012: 237
- FWCI
- 45.68
- Type
- book
- Language
- en
- Open access
- closed
- Locations
- Society for Industrial and Applied Mathematics eBooks (publishedVersion)
- CERN Document Server (European Organization for Nuclear Research) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Matrix Theory and Algorithms
- Physical Sciences › Mathematics › Applied Mathematics › Statistical and numerical algorithms
- Physical Sciences › Mathematics › Numerical Analysis › Advanced Optimization Algorithms Research
- Keywords
- least-squares problems, linear least squares, nonlinear least squares, numerical linear algebra, iterative methods, singular value decomposition, QR decomposition, condition number, ill-conditioned problems
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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