scholarly journals MEG Connectivity and Power Detections with Minimum Norm Estimates Require Different Regularization Parameters

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Ana-Sofía Hincapié ◽  
Jan Kujala ◽  
Jérémie Mattout ◽  
Sebastien Daligault ◽  
Claude Delpuech ◽  
...  
Keyword(s):  
Tikhonov Regularization ◽  
Spectral Power ◽  
Regularization Parameter ◽  
Signal To Noise Ratio ◽  
Minimum Norm ◽  
Norm Estimates ◽  
Source Data ◽  
Ill Posed ◽  
Source Level ◽  
Oscillatory Coupling

Minimum Norm Estimation (MNE) is an inverse solution method widely used to reconstruct the source time series that underlie magnetoencephalography (MEG) data. MNE addresses the ill-posed nature of MEG source estimation through regularization (e.g., Tikhonov regularization). Selecting the best regularization parameter is a critical step. Generally, once set, it is common practice to keep the same coefficient throughout a study. However, it is yet to be known whether the optimal lambda for spectral power analysis of MEG source data coincides with the optimal regularization for source-level oscillatory coupling analysis. We addressed this question via extensive Monte-Carlo simulations of MEG data, where we generated 21,600 configurations of pairs of coupled sources with varying sizes, signal-to-noise ratio (SNR), and coupling strengths. Then, we searched for the Tikhonov regularization coefficients (lambda) that maximize detection performance for (a) power and (b) coherence. For coherence, the optimal lambda was two orders of magnitude smaller than the best lambda for power. Moreover, we found that the spatial extent of the interacting sources and SNR, but not the extent of coupling, were the main parameters affecting the best choice for lambda. Our findings suggest using less regularization when measuring oscillatory coupling compared to power estimation.

scholarly journals A modified Tikhonov regularization method based on Hermite expansion for solving the Cauchy problem of the Laplace equation

2020 ◽  
Vol 18 (1) ◽  
pp. 1685-1697
Author(s):  
Zhenyu Zhao ◽  
Lei You ◽  
Zehong Meng
Keyword(s):  
Cauchy Problem ◽  
Laplace Equation ◽  
Tikhonov Regularization ◽  
Regularization Method ◽  
Regularization Parameter ◽  
Solution Process ◽  
Tikhonov Regularization Method ◽  
Hermite Expansion ◽  
The Cauchy Problem ◽  
Ill Posed

Abstract In this paper, a Cauchy problem for the Laplace equation is considered. We develop a modified Tikhonov regularization method based on Hermite expansion to deal with the ill posed-ness of the problem. The regularization parameter is determined by a discrepancy principle. For various smoothness conditions, the solution process of the method is uniform and the convergence rate can be obtained self-adaptively. Numerical tests are also carried out to verify the effectiveness of the method.

The Regularization Method Based on Complex Collinearity Diagnostics and Metrics

2013 ◽  
Vol 416-417 ◽  
pp. 1393-1398
Author(s):  
Chao Zhong Ma ◽  
Yong Wei Gu ◽  
Ji Fu ◽  
Yuan Lu Du ◽  
Qing Ming Gui
Keyword(s):  
Tikhonov Regularization ◽  
Regularization Method ◽  
Regularization Parameter ◽  
Measurement Data ◽  
Tikhonov Regularization Method ◽  
Parameter Determination ◽  
Selection Methods ◽  
Ill Posed ◽  
Collinearity Diagnostics ◽  
Regularization Matrix

In a large number of measurement data processing, the ill-posed problem is widespread. For such problems, this paper introduces the solution of ill-posed problem of the unity of expression and Tikhonov regularization method, and then to re-collinearity diagnostics and metrics based on proposed based on complex collinearity diagnostics and the metric regularization method is given regularization matrix selection methods and regularization parameter determination formulas. Finally, it uses a simulation example to verify the effectiveness of the method.

A fast multilevel iteration method for solving linear ill-posed integral equations

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Hongqi Yang ◽  
Rong Zhang
Keyword(s):  
Approximate Solution ◽  
Linear System ◽  
Integral Equations ◽  
Tikhonov Regularization ◽  
Iteration Method ◽  
Noise Level ◽  
Regularization Parameter ◽  
Ill Posed ◽  
Linear Integral ◽  
Linear Integral Equations

Abstract We propose a new concept of noise level: R ⁢ ( K * ) \mathcal{R}(K^{*}) -noise level for ill-posed linear integral equations in Tikhonov regularization, which extends the range of regularization parameter. This noise level allows us to choose a more suitable regularization parameter. Moreover, we also analyze error estimates of the approximate solution with respect to this noise level. For ill-posed integral equations, finding fast and effective numerical methods is a challenging problem. For this, we formulate a matrix truncated strategy based on multiscale Galerkin method to generate the linear system of Tikhonov regularization for ill-posed linear integral equations, which greatly reduce the computational complexity. To further reduce the computational cost, a fast multilevel iteration method for solving the linear system is established. At the same time, we also prove convergence rates of the approximate solution obtained by this fast method with respect to the R ⁢ ( K * ) \mathcal{R}(K^{*}) -noise level under the balance principle. By numerical results, we show that R ⁢ ( K * ) \mathcal{R}(K^{*}) -noise level is very useful and the proposed method is a fast and effective method, respectively.

scholarly journals Regularized inversion of full tensor magnetic gradient data

2016 ◽  
pp. 13-20
Author(s):  
Я. Ван ◽  
Д.В. Лукьяненко ◽  
А.Г. Ягола
Keyword(s):  
Tikhonov Regularization ◽  
Conjugate Gradient ◽  
Gradient Method ◽  
Regularization Parameter ◽  
Three Dimensional ◽  
Fredholm Integral Equations ◽  
Minimization Method ◽  
Magnetic Gradient ◽  
Regularized Inversion ◽  
Ill Posed

Рассматриваются особенности численной реализации решения трехмерной обратной задачи обращения полных тензорных магнитно-градиентных данных, которая моделируется системой двух трехмерных интегральных уравнений Фредгольма 1-го рода. Для решения этой некорректно поставленной задачи применяется алгоритм, основанный на минимизации функционала А.Н. Тихонова. В качестве метода минимизации используется метод сопряженных градиентов. Выбор параметра регуляризации осуществляется в соответствии с версией обобщенного принципа невязки, в которой учитываются ошибки округления, существенные при решении задач большой размерности. Features of numerical solution of the three-dimensional ill-posed problem devoted to the inversion of full tensor magnetic gradient data are considered. This problem is simulated by a system of two three-dimensional Fredholm integral equations of the first kind. The Tikhonov regularization is applied to solve this ill-posed problem. The conjugate gradient method is used as a minimization method. The choice of the regularization parameter is realized according to the generalized residual principle with consideration of round-off errors capable of affecting the final result of calculations significantly.

scholarly journals On tensor GMRES and Golub-Kahan methods via the T-product for color image processing

2021 ◽  
Vol 37 ◽  
pp. 524-543
Author(s):  
Mohamed El Guide ◽  
Alaa El Ichi ◽  
Khalide Jbilou ◽  
Rachid Sadaka
Keyword(s):  
Tikhonov Regularization ◽  
Cross Validation ◽  
Krylov Subspace ◽  
Color Image ◽  
Regularization Parameter ◽  
Color Image Processing ◽  
Regularization Technique ◽  
Image Sequence Processing ◽  
Ill Posed ◽  
Selection Of

The present paper is concerned with developing tensor iterative Krylov subspace methods to solve large multi-linear tensor equations. We use the T-product for two tensors to define tensor tubal global Arnoldi and tensor tubal global Golub-Kahan bidiagonalization algorithms. Furthermore, we illustrate how tensor-based global approaches can be exploited to solve ill-posed problems arising from recovering blurry multichannel (color) images and videos, using the so-called Tikhonov regularization technique, to provide computable approximate regularized solutions. We also review a generalized cross-validation and discrepancy principle type of criterion for the selection of the regularization parameter in the Tikhonov regularization. Applications to image sequence processing are given to demonstrate the efficiency of the algorithms.

A unified treatment for Tikhonov regularization using a general stabilizing operator

2015 ◽  
Vol 13 (02) ◽  
pp. 201-215
Author(s):  
M. T. Nair
Keyword(s):  
Tikhonov Regularization ◽  
Regularization Parameter ◽  
Optimal Error Estimates ◽  
Bounded Linear ◽  
Densely Defined Operator ◽  
Special Cases ◽  
Ill Posed ◽  
Residual Norm ◽  
Well Posed ◽  
Densely Defined

While dealing with the problem of solving an ill-posed operator equation Tx = y, where T : X → Y is a bounded linear operator between Hilbert spaces X and Y, one looks for a stable method for approximating [Formula: see text], a least-residual norm solution which minimizes a seminorm x ↦ ‖Lx‖, where L : D(L) ⊆ X → X is a (possibly unbounded) closed densely defined operator in X. If the operators T and L satisfy a completion condition ‖Tx‖2 + ‖Lx‖2 ≥ γ‖x‖2 for all x ∈ D(L*L) for some constant γ > 0, then Tikhonov regularization is one of the simple and widely used of such procedures in which the regularized solution is obtained by solving a well-posed equation [Formula: see text] where yδ is a noisy data and α > 0 is the regularization parameter to be chosen appropriately. We prescribe a condition on (T, L) which unifies the analysis for ordinary Tikhonov regularization, that is, L = I, and also the case of L = Bs with B being a strictly positive closed densely defined unbounded operator which generates a Hilbert scale {Xt}t>0. Under the new framework, we provide estimates for the best possible worst error and order optimal error estimates for the regularized solutions under certain general source condition which incorporates in its fold many existing results as special cases, by choosing regularization parameter using a Morozov-type discrepancy principle.

scholarly journals Penalty-based smoothness conditions in convex variational regularization

2019 ◽  
Vol 27 (2) ◽  
pp. 283-300 ◽  
Author(s):  
Bernd Hofmann ◽  
Stefan Kindermann ◽  
Peter Mathé
Keyword(s):  
Banach Space ◽  
Variational Inequalities ◽  
Tikhonov Regularization ◽  
Hilbert Spaces ◽  
Noise Level ◽  
Regularization Parameter ◽  
The Family ◽  
Ill Posed ◽  
General Convex ◽  
Variational Regularization

Abstract The authors study Tikhonov regularization of linear ill-posed problems with a general convex penalty defined on a Banach space. It is well known that the error analysis requires smoothness assumptions. Here such assumptions are given in form of inequalities involving only the family of noise-free minimizers along the regularization parameter and the (unknown) penalty-minimizing solution. These inequalities control, respectively, the defect of the penalty, or likewise, the defect of the whole Tikhonov functional. The main results provide error bounds for a Bregman distance, which split into two summands: the first smoothness-dependent term does not depend on the noise level, whereas the second term includes the noise level. This resembles the situation of standard quadratic Tikhonov regularization in Hilbert spaces. It is shown that variational inequalities, as these were studied recently, imply the validity of the assumptions made here. Several examples highlight the results in specific applications.

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