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Journal articles on the topic 'Parameter of regularization'

Author: Grafiati

Published: 4 June 2021

Last updated: 6 February 2022

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1

Ito, Kazufumi, Bangti Jin, and Tomoya Takeuchi. "A Regularization Parameter for Nonsmooth Tikhonov Regularization." SIAM Journal on Scientific Computing 33, no. 3 (2011): 1415–38. http://dx.doi.org/10.1137/100790756.

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2

Hyötyniemi, Heikki. "Regularization of Parameter Estimation." IFAC Proceedings Volumes 29, no. 1 (1996): 4652–57. http://dx.doi.org/10.1016/s1474-6670(17)58416-5.

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3

Ito, Kazufumi, Bangti Jin, and Tomoya Takeuchi. "Multi-parameter Tikhonov regularization." Methods and Applications of Analysis 18, no. 1 (2011): 31–46. http://dx.doi.org/10.4310/maa.2011.v18.n1.a2.

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4

Xu, Xiaowei, and Ting Bu. "An Adaptive Parameter Choosing Approach for Regularization Model." International Journal of Pattern Recognition and Artificial Intelligence 32, no. 08 (2018): 1859013. http://dx.doi.org/10.1142/s0218001418590139.

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The choice of regularization parameters is a troublesome issue for most regularization methods, e.g. Tikhonov regularization method, total variation (TV) method, etc. An appropriate parameter for a certain regularization approach can obtain fascinating results. However, general methods of choosing parameters, e.g. Generalized Cross Validation (GCV), cannot get more precise results in practical applications. In this paper, we consider exploiting the more appropriate regularization parameter within a possible range, and apply the estimated parameter to Tikhonov model. In the meanwhile, we obtain

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5

Omer, Hammad, Mahmood Qureshi, and Robert J. Dickinson. "Regularization-based SENSE reconstruction and choice of regularization parameter." Concepts in Magnetic Resonance Part A 44, no. 2 (2015): 67–73. http://dx.doi.org/10.1002/cmr.a.21328.

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6

Hanhela, Matti, Olli Gröhn, Mikko Kettunen, Kati Niinimäki, Marko Vauhkonen, and Ville Kolehmainen. "Data-Driven Regularization Parameter Selection in Dynamic MRI." Journal of Imaging 7, no. 2 (2021): 38. http://dx.doi.org/10.3390/jimaging7020038.

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In dynamic MRI, sufficient temporal resolution can often only be obtained using imaging protocols which produce undersampled data for each image in the time series. This has led to the popularity of compressed sensing (CS) based reconstructions. One problem in CS approaches is determining the regularization parameters, which control the balance between data fidelity and regularization. We propose a data-driven approach for the total variation regularization parameter selection, where reconstructions yield expected sparsity levels in the regularization domains. The expected sparsity levels are

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7

Furukawa, Tomonari. "Parameter identification with weightless regularization." International Journal for Numerical Methods in Engineering 52, no. 3 (2001): 219–38. http://dx.doi.org/10.1002/nme.215.

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8

Sanders, Toby. "Parameter selection for HOTV regularization." Applied Numerical Mathematics 125 (March 2018): 1–9. http://dx.doi.org/10.1016/j.apnum.2017.10.010.

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9

Krawczyk-Stańdo, Dorota, and Marek Rudnicki. "Regularization Parameter Selection in Discrete Ill-Posed Problems — The Use of the U-Curve." International Journal of Applied Mathematics and Computer Science 17, no. 2 (2007): 157–64. http://dx.doi.org/10.2478/v10006-007-0014-3.

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Regularization Parameter Selection in Discrete Ill-Posed Problems — The Use of the U-CurveTo obtain smooth solutions to ill-posed problems, the standard Tikhonov regularization method is most often used. For the practical choice of the regularization parameter α we can then employ the well-known L-curve criterion, based on the L-curve which is a plot of the norm of the regularized solution versus the norm of the corresponding residual for all valid regularization parameters. This paper proposes a new criterion for choosing the regularization parameter α, based on the so-called U-curve. A compa

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10

Zhu, Dixian, Changjie Cai, Tianbao Yang, and Xun Zhou. "A Machine Learning Approach for Air Quality Prediction: Model Regularization and Optimization." Big Data and Cognitive Computing 2, no. 1 (2018): 5. http://dx.doi.org/10.3390/bdcc2010005.

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In this paper, we tackle air quality forecasting by using machine learning approaches to predict the hourly concentration of air pollutants (e.g., ozone, particle matter ( PM 2.5 ) and sulfur dioxide). Machine learning, as one of the most popular techniques, is able to efficiently train a model on big data by using large-scale optimization algorithms. Although there exist some works applying machine learning to air quality prediction, most of the prior studies are restricted to several-year data and simply train standard regression models (linear or nonlinear) to predict the hourly air polluti

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11

Kravaris, Costas, and John H. Seinfeld. "Identification of Parameters in Distributed Parameter Systems by Regularization." SIAM Journal on Control and Optimization 23, no. 2 (1985): 217–41. http://dx.doi.org/10.1137/0323017.

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12

Gould, N. I. M., M. Porcelli, and P. L. Toint. "Updating the regularization parameter in the adaptive cubic regularization algorithm." Computational Optimization and Applications 53, no. 1 (2011): 1–22. http://dx.doi.org/10.1007/s10589-011-9446-7.

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13

Yang, Xiaocheng, Zhenyi Yang, Jingye Yan, Lin Wu, and Mingfeng Jiang. "Multi-Parameter Regularization Method for Synthetic Aperture Imaging Radiometers." Remote Sensing 13, no. 3 (2021): 382. http://dx.doi.org/10.3390/rs13030382.

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Synthetic aperture imaging radiometers (SAIRs) are powerful passive microwave systems for high-resolution imaging by use of synthetic aperture technique. However, the ill-posed inverse problem for SAIRs makes it difficult to reconstruct the high-precision brightness temperature map. The traditional regularization methods add a unique penalty to all the frequency bands of the solution, which may cause the reconstructed result to be too smooth to retain certain features of the original brightness temperature map such as the edge information. In this paper, a multi-parameter regularization method

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14

Mei, Yue, Mahsa Tajderi, and Sevan Goenezen. "Regularizing Biomechanical Maps for Partially Known Material Properties." International Journal of Applied Mechanics 09, no. 02 (2017): 1750020. http://dx.doi.org/10.1142/s175882511750020x.

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We present the solution of the inverse problem for partially known elastic modulus values, e.g., the elastic modulus is known in some small region on the boundary of the domain from measurements. The inverse problem is posed as a constrained minimization problem and regularized with two different regularization types. In particular, the total variation diminishing (TVD) and the total contrast diminishing (TCD) regularizations are employed. We test both regularization strategies with theoretical diseased tissues, such as a stiff tumor surrounded by healthy background tissue and an atherosclerot

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15

Wang, Sicheng, Sixun Huang, and Hanxian Fang. "Dual-parameter regularization method in three-dimensional ionospheric reconstruction." Annales Geophysicae 36, no. 5 (2018): 1255–66. http://dx.doi.org/10.5194/angeo-36-1255-2018.

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Abstract. Ionospheric tomography based on the total electron content (TEC) data along the ray path from Global Navigation Satellite Systems (GNSS) satellites to ground receivers is a typical ill-posed inverse problem. The regularization method is an effective method to solve this problem, which incorporates prior constraints to approximate the real ionospheric variations. When two or more prior constraints are used, the corresponding multiple regularization parameters are introduced in the cost functional. Assuming that the ionospheric spatial variations can be separable in the horizontal and

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16

LU, S., S. V. PEREVERZEV, and U. TAUTENHAHN. "Dual Regularized Total Least Squares And Multi-Parameter Regularization." Computational Methods in Applied Mathematics 8, no. 3 (2008): 253–62. http://dx.doi.org/10.2478/cmam-2008-0018.

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AbstractIn this paper we continue our study of solving ill-posed problems with a noisy right-hand side and a noisy operator. Regularized approximations are obtained by Tikhonov regularization with differential operators and by dual regularized total least squares (dual RTLS) which can be characterized as a special multi-parameter regularization method where one of the two regularization parameters is negative. We report on order optimality results for both regularized approximations, discuss compu-tational aspects, provide special algorithms and show by experiments that dual RTLS is competitiv

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17

Giesen, Joachim, Sӧren Laue, Andreas Lӧhne, and Christopher Schneider. "Using Benson’s Algorithm for Regularization Parameter Tracking." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 3689–96. http://dx.doi.org/10.1609/aaai.v33i01.33013689.

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Regularized loss minimization, where a statistical model is obtained from minimizing the sum of a loss function and weighted regularization terms, is still in widespread use in machine learning. The statistical performance of the resulting models depends on the choice of weights (regularization parameters) that are typically tuned by cross-validation. For finding the best regularization parameters, the regularized minimization problem needs to be solved for the whole parameter domain. A practically more feasible approach is covering the parameter domain with approximate solutions of the loss m

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18

Gao, Wei, Kaiping Yu, and Ying Wu. "A New Method for Optimal Regularization Parameter Determination in the Inverse Problem of Load Identification." Shock and Vibration 2016 (2016): 1–16. http://dx.doi.org/10.1155/2016/7328969.

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According to the regularization method in the inverse problem of load identification, a new method for determining the optimal regularization parameter is proposed. Firstly, quotient function (QF) is defined by utilizing the regularization parameter as a variable based on the least squares solution of the minimization problem. Secondly, the quotient function method (QFM) is proposed to select the optimal regularization parameter based on the quadratic programming theory. For employing the QFM, the characteristics of the values of QF with respect to the different regularization parameters are t

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19

Yan Xuefei, 阎雪飞, 许廷发 Xu Tingfa, and 白廷柱 Bai Tingzhu. "Varying-Parameter Tikhonov Regularization Image Restoration." Laser & Optoelectronics Progress 50, no. 5 (2013): 051001. http://dx.doi.org/10.3788/lop50.051001.

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20

Fraiture, Luc. "Regularization of Minimum Parameter Attitude Estimation." Journal of Guidance, Control, and Dynamics 32, no. 3 (2009): 1029–34. http://dx.doi.org/10.2514/1.41687.

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21

Jin, Bangti, and Peter Maass. "Sparsity regularization for parameter identification problems." Inverse Problems 28, no. 12 (2012): 123001. http://dx.doi.org/10.1088/0266-5611/28/12/123001.

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22

Mcmasters, R. L., and J. V. Beck. "Using derivative regularization in parameter estimation." Inverse Problems in Engineering 8, no. 4 (2000): 365–90. http://dx.doi.org/10.1080/174159700088027736.

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23

Zama, Fabiana. "Parameter Identification by Iterative Constrained Regularization." Journal of Physics: Conference Series 657 (November 16, 2015): 012002. http://dx.doi.org/10.1088/1742-6596/657/1/012002.

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24

Fornasier, Massimo, Valeriya Naumova, and Sergei V. Pereverzyev. "Parameter Choice Strategies for Multipenalty Regularization." SIAM Journal on Numerical Analysis 52, no. 4 (2014): 1770–94. http://dx.doi.org/10.1137/130930248.

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25

Kang, Myeongmin, Miyoun Jung, and Myungjoo Kang. "Higher-order regularization based image restoration with automatic regularization parameter selection." Computers & Mathematics with Applications 76, no. 1 (2018): 58–80. http://dx.doi.org/10.1016/j.camwa.2018.04.004.

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26

Reginska, Teresa. "DISCREPANCY SETS FOR COMBINED LEAST SQUARES PROJECTION AND TIKHONOV REGULARIZATION." Mathematical Modelling and Analysis 22, no. 2 (2017): 202–12. http://dx.doi.org/10.3846/13926292.2017.1289987.

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To solve a linear ill-posed problem, a combination of the finite dimensional least squares projection method and the Tikhonov regularization is considered. The dimension of the projection is treated as the second parameter of regularization. A two-parameter discrepancy principle defines a discrepancy set for any data error bound. The aim of the paper is to describe this set and to indicate its subset such that for regularization parameters from this subset the related regularized solution has the same order of accuracy as the Tikhonov regularization with the standard discrepancy principle but

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27

CHEN, WEN-SHENG, PONG C. YUEN, and JIAN HUANG. "A NEW REGULARIZED LINEAR DISCRIMINANT ANALYSIS METHOD TO SOLVE SMALL SAMPLE SIZE PROBLEMS." International Journal of Pattern Recognition and Artificial Intelligence 19, no. 07 (2005): 917–35. http://dx.doi.org/10.1142/s0218001405004344.

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This paper presents a new regularization technique to deal with the small sample size (S3) problem in linear discriminant analysis (LDA) based face recognition. Regularization on the within-class scatter matrix Sw has been shown to be a good direction for solving the S3 problem because the solution is found in full space instead of a subspace. The main limitation in regularization is that a very high computation is required to determine the optimal parameters. In view of this limitation, this paper re-defines the three-parameter regularization on the within-class scatter matrix [Formula: see t

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28

INAGAKI, T., D. KIMURA, H. KOHYAMA, and A. KVINIKHIDZE. "REGULARIZATION PARAMETER INDEPENDENT ANALYSIS IN NAMBU–JONA-LASINIO MODEL." International Journal of Modern Physics A 28, no. 31 (2013): 1350164. http://dx.doi.org/10.1142/s0217751x13501649.

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Nambu–Jona-Lasinio model used to investigate low energy phenomena is nonrenormalizable, therefore the results depend on the regularization parameter in general. A possibility of the finite in four-dimensional limit and even the in regularization parameter (this is dimension in the dimensional regularization scheme) independent analysis is shown in the leading order of the 1/Nc expansion.

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29

Saud, Arjun Singh, and Subarna Shakya. "Analysis of L2 Regularization Hyper Parameter for Stock Price Prediction." Journal of Institute of Science and Technology 26, no. 1 (2021): 83–88. http://dx.doi.org/10.3126/jist.v26i1.37830.

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Nowadays stock price prediction is an active area of research among machine learning researchers. One of the main problems with machine learning models is overfitting. Regularization techniques are widely used approaches to avoid over-fitted models. L2 regularization is one of the most popular and widely used regularization techniques. Regularization hyperparameter (ʎ) is one key parameter to be optimized for a well-generalized machine learning model. Hyperparameters can’t be learned by machine learning models during the learning process. We need to find their optimal value through experiments

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30

Yang, Suhua, Xingjun Luo, Chunmei Zeng, Zhihai Xu, and Wenyu Hu. "On the Parameter Choice in the Multilevel Augmentation Method." Computational Methods in Applied Mathematics 20, no. 3 (2020): 555–71. http://dx.doi.org/10.1515/cmam-2018-0189.

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AbstractIn this paper, we apply the multilevel augmentation method for solving ill-posed Fredholm integral equations of the first kind via iterated Tikhonov regularization method. The method leads to fast solutions of the discrete regularization methods for the equations. The convergence rates of iterated Tikhonov regularization are achieved by using a modified parameter choice strategy. Finally, numerical experiments are given to illustrate the efficiency of the method.

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31

Zhu, Zhining, Guangcheng Cai, and You-Wei Wen. "Adaptive Box-Constrained Total Variation Image Restoration Using Iterative Regularization Parameter Adjustment Method." International Journal of Pattern Recognition and Artificial Intelligence 29, no. 07 (2015): 1554003. http://dx.doi.org/10.1142/s0218001415540038.

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In this paper, we consider the problem of image restoration with box-constraints. Image restoration problem is ill-conditioned and the regularization approach has widely been used to stabilize the solution. The restored image highly depends on the choice of the regularization parameter. The regularization parameter is generally determined by trial-and-error method when no true original image is available. Obviously, it is time consuming. The main aim in this paper is to develop an algorithm to choose the regularization parameter automatically when the box-constraints are imposed. In the propos

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32

Humayoo, Mahammad, та Xueqi Cheng. "Parameter Estimation with the Ordered ℓ2 Regularization via an Alternating Direction Method of Multipliers". Applied Sciences 9, № 20 (2019): 4291. http://dx.doi.org/10.3390/app9204291.

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Regularization is a popular technique in machine learning for model estimation and for avoiding overfitting. Prior studies have found that modern ordered regularization can be more effective in handling highly correlated, high-dimensional data than traditional regularization. The reason stems from the fact that the ordered regularization can reject irrelevant variables and yield an accurate estimation of the parameters. How to scale up the ordered regularization problems when facing large-scale training data remains an unanswered question. This paper explores the problem of parameter estimatio

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33

He, Chuan, Changhua Hu, Wei Zhang, Biao Shi, and Xiaoxiang Hu. "Fast Total-Variation Image Deconvolution with Adaptive Parameter Estimation via Split Bregman Method." Mathematical Problems in Engineering 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/617026.

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The total-variation (TV) regularization has been widely used in image restoration domain, due to its attractive edge preservation ability. However, the estimation of the regularization parameter, which balances the TV regularization term and the data-fidelity term, is a difficult problem. In this paper, based on the classical split Bregman method, a new fast algorithm is derived to simultaneously estimate the regularization parameter and to restore the blurred image. In each iteration, the regularization parameter is refreshed conveniently in a closed form according to Morozov’s discrepancy pr

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34

Tang, Jinping, Bo Han, Weimin Han, Bo Bi, and Li Li. "Mixed Total Variation and L1 Regularization Method for Optical Tomography Based on Radiative Transfer Equation." Computational and Mathematical Methods in Medicine 2017 (2017): 1–15. http://dx.doi.org/10.1155/2017/2953560.

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Optical tomography is an emerging and important molecular imaging modality. The aim of optical tomography is to reconstruct optical properties of human tissues. In this paper, we focus on reconstructing the absorption coefficient based on the radiative transfer equation (RTE). It is an ill-posed parameter identification problem. Regularization methods have been broadly applied to reconstruct the optical coefficients, such as the total variation (TV) regularization and the L1 regularization. In order to better reconstruct the piecewise constant and sparse coefficient distributions, TV and L1 no

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35

Guo, Jiangfeng, Ranhong Xie, Youlong Zou, Guowen Jin, Lun Gao, and Chenyu Xu. "A new method for NMR data inversion based on double-parameter regularization." GEOPHYSICS 83, no. 5 (2018): JM39—JM49. http://dx.doi.org/10.1190/geo2017-0394.1.

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Nuclear magnetic resonance (NMR) technology plays a significant role in petroleum exploration. NMR data can be processed using inversion methods to reflect the relaxation information of all the components. We have developed a new double-parameter regularization (DPR) method for the inversion of NMR data, whose regularization terms consist of Tikhonov regularization and maximum entropy regularization. The objective function for the DPR method was solved using the Levenberg-Marquardt method, the proportional coefficient of the regularization parameter was obtained using an iteration procedure, a

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36

Ma, Tian-Hui, Ting-Zhu Huang, and Xi-Le Zhao. "New Regularization Models for Image Denoising with a Spatially Dependent Regularization Parameter." Abstract and Applied Analysis 2013 (2013): 1–15. http://dx.doi.org/10.1155/2013/729151.

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We consider simultaneously estimating the restored image and the spatially dependent regularization parameter which mutually benefit from each other. Based on this idea, we refresh two well-known image denoising models: the LLT model proposed by Lysaker et al. (2003) and the hybrid model proposed by Li et al. (2007). The resulting models have the advantage of better preserving image regions containing textures and fine details while still sufficiently smoothing homogeneous features. To efficiently solve the proposed models, we consider an alternating minimization scheme to resolve the original

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37

Sugiyama, Masashi, and Hidemitsu Ogawa. "Optimal design of regularization term and regularization parameter by subspace information criterion." Neural Networks 15, no. 3 (2002): 349–61. http://dx.doi.org/10.1016/s0893-6080(02)00022-9.

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38

Renaut, Rosemary A., Iveta Hnětynková, and Jodi Mead. "Regularization parameter estimation for large-scale Tikhonov regularization using a priori information." Computational Statistics & Data Analysis 54, no. 12 (2010): 3430–45. http://dx.doi.org/10.1016/j.csda.2009.05.026.

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39

Christophe, Chiza, Bua Anthony, Goodluck Kapyela, and Abdi Abdalla. "Comparative Analysis of Multiplicative and Additive Noise Based Automated Regularizations in Non-Linear Diffusion Image Reconstruction." Tanzania Journal of Engineering and Technology 39, no. 2 (2020): 116–26. http://dx.doi.org/10.52339/tjet.v39i2.699.

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Multiplicative and additive noises are often introduced in image signals during the image acquisition process and result into degradation of image features. The work done by Perona and Malik in 1990 and its modified versions revolutionized the way through which noises or speckles are removed. The Perona-Malik model requires tuning of the regularization parameter to control and prevent staircase artifacts in restored images. The current manual tuning is a challenging and time consuming practice when a long queue of images is registered for processing. Attempt to automate the regularization para

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40

Li, Dong, and Lei Gong. "Sensor Alignment for Ballistic Trajectory Estimation via Sparse Regularization." Information 9, no. 10 (2018): 255. http://dx.doi.org/10.3390/info9100255.

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Sensor alignment plays a key role in the accurate estimation of the ballistic trajectory. A sparse regularization-based sensor alignment method coupled with the selection of a regularization parameter is proposed in this paper. The sparse regularization model is established by combining the traditional model of trajectory estimation with the sparse constraint of systematic errors. The trajectory and the systematic errors are estimated simultaneously by using the Newton algorithm. The regularization parameter in the model is crucial to the accuracy of trajectory estimation. Stein’s unbiased ris

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41

Wang, Zewen. "Multi-parameter Tikhonov regularization and model function approach to the damped Morozov principle for choosing regularization parameters." Journal of Computational and Applied Mathematics 236, no. 7 (2012): 1815–32. http://dx.doi.org/10.1016/j.cam.2011.10.014.

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42

Bashier, Eihab B. M. "Estimation of the Optimal Regularization Parameters in Optimal Control Problems with time delay." JOURNAL OF ADVANCES IN MATHEMATICS 12, no. 9 (2016): 6589–602. http://dx.doi.org/10.24297/jam.v12i9.129.

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In this paper we use the L-curve method and the Morozov discrepancy principle for the estimation of the regularization parameter in the regularization of time-delayed optimal control computation. Zeroth order, first order and second order differential operators are considered. Two test examples show that the L-curve method and the two discrepancy principles give close estimations for the regularization parameters.

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43

Wang, Chongwen, and Chengbin Du. "An Improved Sensitivity Method for the Simultaneous Identification of Unknown Parameters and External Loads of Nonlinear Structures." Shock and Vibration 2018 (November 7, 2018): 1–12. http://dx.doi.org/10.1155/2018/1737594.

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Because structures may be subject to unknown loads and may simultaneously involve unknown parameters and because simple load identification or parameter identification algorithms cannot be applied under such conditions, it is necessary to seek algorithms that can simultaneously identify unknown parameters and external loads of structures. The sensitivity method is one of them, and this paper extends this method to nonlinear structures. In addition, the key issues associated with the sensitivity method are systematically studied, and suggestions for improvement are put forward, including the us

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44

LEE, TAEYEON. "SHORT DISTANCE REGULARIZATION." Modern Physics Letters A 11, no. 12 (1996): 995–1000. http://dx.doi.org/10.1142/s0217732396001028.

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A regularization scheme is discussed, in which the naive momentum cutoff of an internal loop integral ∫d4q is modified to ∫d4qe±iq.ε. The uv divergence is suppressed by high frequency oscillation of e±iq.ε. Since the regulator is removed as ε→0, logarithmic divergence is expressed as logarithmic singularity of ε such as ln (μ|ε|) where μ is an arbitrary mass parameter. With ε as a mere parameter describing uv divergence, this regularization method does not break the important Poincaré invariance and gauge symmetry. This method also allows easier evaluation of loop integrals than the dimensiona

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45

Roy, Indrajit G. "Interpreting potential field anomaly of an isolated source of regular geometry revisited." GEOPHYSICS 82, no. 5 (2017): IM41—IM48. http://dx.doi.org/10.1190/geo2016-0571.1.

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I have developed an improved practical method for interpreting a symmetrical-shaped potential field anomaly due to an isolated source body of regular geometric configuration. The method uses the first-order horizontal derivative of the logarithmically transformed absolute value of the anomaly in estimating the source-body parameters, such as the location, depth of burial, and shape factor. To tackle noise in data, a regularization technique is designed, which ensures a robust estimate of the first-order derivative of logarithmically transformed data. The regularization technique uses an optima

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46

Sitdikov, I. T., and A. S. Krylov. "Variational image deringing using varying regularization parameter." Pattern Recognition and Image Analysis 25, no. 1 (2015): 96–100. http://dx.doi.org/10.1134/s1054661815010186.

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47

Doktorski, L. "L2-SVM: Dependence on the regularization parameter." Pattern Recognition and Image Analysis 21, no. 2 (2011): 254–57. http://dx.doi.org/10.1134/s1054661811020258.

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48

Watzenig, D., B. Brandstatter, and G. Holler. "Adaptive Regularization Parameter Adjustment for Reconstruction Problems." IEEE Transactions on Magnetics 40, no. 2 (2004): 1116–19. http://dx.doi.org/10.1109/tmag.2004.824557.

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49

Correia, Teresa, Adam Gibson, Martin Schweiger, and Jeremy Hebden. "Selection of regularization parameter for optical topography." Journal of Biomedical Optics 14, no. 3 (2009): 034044. http://dx.doi.org/10.1117/1.3156839.

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50

Zhang, Yiyun, Runze Li, and Chih-Ling Tsai. "Regularization Parameter Selections via Generalized Information Criterion." Journal of the American Statistical Association 105, no. 489 (2010): 312–23. http://dx.doi.org/10.1198/jasa.2009.tm08013.

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