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Weighted minimum norm mitigates the superficial bias by weighting current sources by the norm of the EEG/MEG signals they can generate with a unitary magnitude.
“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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- 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
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