Unification of sparse Bayesian learning algorithms for electromagnetic brain imaging with the majorization minimization framework
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Stefan Haufe | Klaus-Robert Müller | Gitta Kutyniok | Srikantan S. Nagarajan | Chang Cai | Ali Hashemi
[1] Michael Muma,et al. Robust Statistics for Signal Processing , 2018 .
[2] Diethard Pallaschke,et al. Foundations of mathematical optimization : convex analysis without linearity , 1997 .
[3] Gunnar Rätsch,et al. A Mathematical Programming Approach to the Kernel Fisher Algorithm , 2000, NIPS.
[4] Bhaskar D. Rao,et al. An Empirical Bayesian Strategy for Solving the Simultaneous Sparse Approximation Problem , 2007, IEEE Transactions on Signal Processing.
[5] Kensuke Sekihara,et al. Hierarchical multiscale Bayesian algorithm for robust MEG/EEG source reconstruction , 2018, NeuroImage.
[6] K. Matsuura,et al. Selective minimum-norm solution of the biomagnetic inverse problem , 1995, IEEE Transactions on Biomedical Engineering.
[7] Victor Solo,et al. Sparse component selection with application to MEG source localization , 2013, 2013 IEEE 10th International Symposium on Biomedical Imaging.
[8] Stefan Haufe,et al. Optimizing the regularization for image reconstruction of cerebral diffuse optical tomography , 2014, Journal of biomedical optics.
[9] E. Halgren,et al. Dynamic Statistical Parametric Mapping Combining fMRI and MEG for High-Resolution Imaging of Cortical Activity , 2000, Neuron.
[10] R. Tibshirani,et al. Sparse inverse covariance estimation with the graphical lasso. , 2008, Biostatistics.
[11] Barbara Vantaggi,et al. Brain Activity Mapping from MEG Data via a Hierarchical Bayesian Algorithm with Automatic Depth Weighting , 2018, Brain Topography.
[12] Radford M. Neal. Pattern Recognition and Machine Learning , 2007, Technometrics.
[13] Lourens J. Waldorp,et al. Spatiotemporal EEG/MEG source analysis based on a parametric noise covariance model , 2002, IEEE Transactions on Biomedical Engineering.
[14] C. Villani. Optimal Transport: Old and New , 2008 .
[15] Jérôme Idier,et al. Algorithms for Nonnegative Matrix Factorization with the β-Divergence , 2010, Neural Computation.
[16] A. Papadopoulos. Metric Spaces, Convexity and Nonpositive Curvature , 2004 .
[17] Shai Ben-David,et al. Understanding Machine Learning: From Theory to Algorithms , 2014 .
[18] Adeel Razi,et al. Bayesian fusion and multimodal DCM for EEG and fMRI , 2019, NeuroImage.
[19] Andrzej Cichocki,et al. Families of Alpha- Beta- and Gamma- Divergences: Flexible and Robust Measures of Similarities , 2010, Entropy.
[20] Bhaskar D. Rao,et al. Sparse Bayesian learning for basis selection , 2004, IEEE Transactions on Signal Processing.
[21] R. Ilmoniemi,et al. Magnetoencephalography-theory, instrumentation, and applications to noninvasive studies of the working human brain , 1993 .
[22] Karl J. Friston,et al. Classical and Bayesian Inference in Neuroimaging: Theory , 2002, NeuroImage.
[23] Wei Wu,et al. Bayesian Machine Learning: EEG\/MEG signal processing measurements , 2016, IEEE Signal Processing Magazine.
[24] D. Hunter,et al. A Tutorial on MM Algorithms , 2004 .
[25] Prabhu Babu,et al. Robust Estimation of Structured Covariance Matrix for Heavy-Tailed Elliptical Distributions , 2015, IEEE Transactions on Signal Processing.
[26] D. Lehmann,et al. Low resolution electromagnetic tomography: a new method for localizing electrical activity in the brain. , 1994, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.
[27] David P. Wipf,et al. Iterative Reweighted 1 and 2 Methods for Finding Sparse Solutions , 2010, IEEE J. Sel. Top. Signal Process..
[28] R D Pascual-Marqui,et al. Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details. , 2002, Methods and findings in experimental and clinical pharmacology.
[29] I F Gorodnitsky,et al. Neuromagnetic source imaging with FOCUSS: a recursive weighted minimum norm algorithm. , 1995, Electroencephalography and clinical neurophysiology.
[30] Hagai Attias,et al. A probabilistic algorithm integrating source localization and noise suppression for MEG and EEG data , 2006, NeuroImage.
[31] A. Dale,et al. Improved Localizadon of Cortical Activity by Combining EEG and MEG with MRI Cortical Surface Reconstruction: A Linear Approach , 1993, Journal of Cognitive Neuroscience.
[32] Trevor J. Hastie,et al. The Graphical Lasso: New Insights and Alternatives , 2011, Electronic journal of statistics.
[33] Daniel Pérez Palomar,et al. Optimization Methods for Financial Index Tracking: From Theory to Practice , 2018, Found. Trends Optim..
[34] Prabhu Babu,et al. Majorization-Minimization Algorithms in Signal Processing, Communications, and Machine Learning , 2017, IEEE Transactions on Signal Processing.
[35] Maher Moakher,et al. A Differential Geometric Approach to the Geometric Mean of Symmetric Positive-Definite Matrices , 2005, SIAM J. Matrix Anal. Appl..
[36] A. Benfenati,et al. Proximal approaches for matrix optimization problems: Application to robust precision matrix estimation , 2017, Signal Process..
[37] Saurabh Khanna,et al. On the Support Recovery of Jointly Sparse Gaussian Sources via Sparse Bayesian Learning , 2017, IEEE Transactions on Information Theory.
[38] David P. Wipf,et al. Variational Bayesian Inference Techniques , 2010, IEEE Signal Processing Magazine.
[39] D. Ruppert. The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2004 .
[40] Daniel P. Palomar,et al. A Signal Processing Perspective of Financial Engineering , 2016, Found. Trends Signal Process..
[41] Robert Oostenveld,et al. The five percent electrode system for high-resolution EEG and ERP measurements , 2001, Clinical Neurophysiology.
[42] Julia P. Owen,et al. Robust Bayesian estimation of the location, orientation, and time course of multiple correlated neural sources using MEG , 2010, NeuroImage.
[43] Esa Ollila,et al. Shrinking the Eigenvalues of M-Estimators of Covariance Matrix , 2021, IEEE Transactions on Signal Processing.
[44] Andreas Ziehe,et al. Combining sparsity and rotational invariance in EEG/MEG source reconstruction , 2008, NeuroImage.
[45] K. Lange,et al. The MM Alternative to EM , 2010, 1104.2203.
[46] C. Stein,et al. Estimation with Quadratic Loss , 1992 .
[47] Björn E. Ottersten,et al. Covariance Matching Estimation Techniques for Array Signal Processing Applications , 1998, Digit. Signal Process..
[48] Daniela Calvetti,et al. Inverse problems: From regularization to Bayesian inference , 2018 .
[49] Julia P. Owen,et al. Performance evaluation of the Champagne source reconstruction algorithm on simulated and real M/EEG data , 2012, NeuroImage.
[50] L. Bregman. The relaxation method of finding the common point of convex sets and its application to the solution of problems in convex programming , 1967 .
[51] Zhi-Quan Luo,et al. A Unified Convergence Analysis of Block Successive Minimization Methods for Nonsmooth Optimization , 2012, SIAM J. Optim..
[52] Alfred O. Hero,et al. Covariance Estimation in High Dimensions Via Kronecker Product Expansions , 2013, IEEE Transactions on Signal Processing.
[53] Yu Huang,et al. The New York Head—A precise standardized volume conductor model for EEG source localization and tES targeting , 2015, NeuroImage.
[54] Sergey M. Plis,et al. A generalized spatiotemporal covariance model for stationary background in analysis of MEG data , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.
[55] A. Gramfort,et al. A hierarchical Bayesian perspective on majorization-minimization for non-convex sparse regression: application to M/EEG source imaging , 2017, Inverse Problems.
[56] Sandeep Kumar,et al. A Unified Framework for Structured Graph Learning via Spectral Constraints , 2019, J. Mach. Learn. Res..
[57] Michael E. Tipping. The Relevance Vector Machine , 1999, NIPS.
[58] Giuseppe Caire,et al. Massive MIMO Unsourced Random Access , 2019, ArXiv.
[59] David P. Wipf,et al. Dual-Space Analysis of the Sparse Linear Model , 2012, NIPS.
[60] Alexandre Gramfort,et al. Automated model selection in covariance estimation and spatial whitening of MEG and EEG signals , 2015, NeuroImage.
[61] Fetsje Bijma,et al. A mathematical approach to the temporal stationarity of background noise in MEG/EEG measurements , 2003, NeuroImage.
[62] Jens Haueisen,et al. The Iterative Reweighted Mixed-Norm Estimate for Spatio-Temporal MEG/EEG Source Reconstruction , 2016, IEEE Transactions on Medical Imaging.
[63] Bhaskar D. Rao,et al. Sparse Signal Recovery With Temporally Correlated Source Vectors Using Sparse Bayesian Learning , 2011, IEEE Journal of Selected Topics in Signal Processing.
[64] Alan L. Yuille,et al. The Concave-Convex Procedure , 2003, Neural Computation.
[65] Wei Yu,et al. Optimization of MIMO Device-to-Device Networks via Matrix Fractional Programming: A Minorization–Maximization Approach , 2018, IEEE/ACM Transactions on Networking.
[66] Kensuke Sekihara,et al. MEG/EEG Source Reconstruction, Statistical Evaluation, and Visualization with NUTMEG , 2011, Comput. Intell. Neurosci..
[67] Bhaskar D. Rao,et al. Latent Variable Bayesian Models for Promoting Sparsity , 2011, IEEE Transactions on Information Theory.
[68] Roberto D. Pascual-Marqui,et al. Discrete, 3D distributed, linear imaging methods of electric neuronal activity. Part 1: exact, zero error localization , 2007, 0710.3341.
[69] Inderjit S. Dhillon,et al. Information-theoretic metric learning , 2006, ICML '07.
[70] João M. F. Xavier,et al. Robust Localization of Nodes and Time-Recursive Tracking in Sensor Networks Using Noisy Range Measurements , 2011, IEEE Transactions on Signal Processing.
[71] D. M. Schmidt,et al. Spatiotemporal noise covariance estimation from limited empirical magnetoencephalographic data , 2006, Physics in medicine and biology.
[72] Giuseppe Caire,et al. Structured Channel Covariance Estimation from Limited Samples in Massive MIMO , 2019, ICC 2020 - 2020 IEEE International Conference on Communications (ICC).
[73] A. Gramfort,et al. Mixed-norm estimates for the M/EEG inverse problem using accelerated gradient methods , 2012, Physics in medicine and biology.
[74] Bhaskar D. Rao,et al. Joint Channel Estimation and Data Detection in MIMO-OFDM Systems: A Sparse Bayesian Learning Approach , 2015, IEEE Transactions on Signal Processing.
[75] Gabriel Peyré,et al. Fast Optimal Transport Averaging of Neuroimaging Data , 2015, IPMI.
[76] T. Rapcsák. Geodesic convexity in nonlinear optimization , 1991 .
[77] Ron Kohavi,et al. A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.
[78] Christoph F. Mecklenbräuker,et al. Multisnapshot Sparse Bayesian Learning for DOA , 2016, IEEE Signal Processing Letters.
[79] Suvrit Sra,et al. Geometric Mean Metric Learning , 2016, ICML.
[80] Nelson J. Trujillo-Barreto,et al. Bayesian model averaging in EEG/MEG imaging , 2004, NeuroImage.
[81] Saurabh Khanna,et al. Rényi divergence based covariance matching pursuit of joint sparse support , 2017, 2017 IEEE 18th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC).
[82] Alexandre Gramfort,et al. Mapping, timing and tracking cortical activations with MEG and EEG: Methods and application to human vision. (Localisation et suivi d'activité fonctionnelle cérébrale en électro et magnétoencéphalographie: Méthodes et applications au système visuel humain) , 2009 .
[83] Shogo Kato,et al. Entropy and Divergence Associated with Power Function and the Statistical Application , 2010, Entropy.
[84] D. Rubin,et al. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .
[85] Alfred O. Hero,et al. On Convergence of Kronecker Graphical Lasso Algorithms , 2012, IEEE Transactions on Signal Processing.
[86] Andrew M. Stuart,et al. Inverse problems: A Bayesian perspective , 2010, Acta Numerica.
[87] Jens Haueisen,et al. Time-frequency mixed-norm estimates: Sparse M/EEG imaging with non-stationary source activations , 2013, NeuroImage.
[88] George Eastman House,et al. Sparse Bayesian Learning and the Relevance Vector Machine , 2001 .
[89] Marco Cuturi,et al. Computational Optimal Transport: With Applications to Data Science , 2019 .
[90] Motoaki Kawanabe,et al. Divergence-Based Framework for Common Spatial Patterns Algorithms , 2014, IEEE Reviews in Biomedical Engineering.
[91] Diethard Pallaschke,et al. Foundations of Mathematical Optimization , 1997 .
[92] Nancy Bertin,et al. Nonnegative Matrix Factorization with the Itakura-Saito Divergence: With Application to Music Analysis , 2009, Neural Computation.
[93] Chang Cai,et al. Robust estimation of noise for electromagnetic brain imaging with the champagne algorithm , 2021, NeuroImage.
[94] Stephen P. Boyd,et al. Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.
[95] Stefan Haufe,et al. Improving EEG Source Localization Through Spatio-Temporal Sparse Bayesian Learning , 2018, 2018 26th European Signal Processing Conference (EUSIPCO).
[96] Richard M. Leahy,et al. Electromagnetic brain mapping , 2001, IEEE Signal Process. Mag..
[97] Giuseppe Caire,et al. Massive MIMO Channel Subspace Estimation From Low-Dimensional Projections , 2015, IEEE Transactions on Signal Processing.
[98] Jens Haueisen,et al. MEG/EEG Source Imaging with a Non-Convex Penalty in the Time-Frequency Domain , 2015, 2015 International Workshop on Pattern Recognition in NeuroImaging.
[99] Cédric Févotte,et al. Majorization-minimization algorithm for smooth Itakura-Saito nonnegative matrix factorization , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[100] Stefan Haufe,et al. Solving the EEG inverse problem based on space–time–frequency structured sparsity constraints , 2015, NeuroImage.
[101] R. Ilmoniemi,et al. Interpreting magnetic fields of the brain: minimum norm estimates , 2006, Medical and Biological Engineering and Computing.
[102] Philippe Forster,et al. Matched and Mismatched Estimation of Kronecker Product of Linearly Structured Scatter Matrices Under Elliptical Distributions , 2021, IEEE Transactions on Signal Processing.
[103] Petre Stoica,et al. On Estimation of Covariance Matrices With Kronecker Product Structure , 2008, IEEE Transactions on Signal Processing.
[104] Nisheeth K. Vishnoi. Geodesic Convex Optimization: Differentiation on Manifolds, Geodesics, and Convexity , 2018, ArXiv.
[105] Alfred O. Hero,et al. Robust Kronecker Product PCA for Spatio-Temporal Covariance Estimation , 2015, IEEE Transactions on Signal Processing.
[106] Ami Wiesel,et al. Structured Robust Covariance Estimation , 2015, Found. Trends Signal Process..
[107] Bin Yu,et al. High-dimensional covariance estimation by minimizing ℓ1-penalized log-determinant divergence , 2008, 0811.3628.
[108] Lourens J. Waldorp,et al. Estimating stationary dipoles from MEG/EEG data contaminated with spatially and temporally correlated background noise , 2001, NeuroImage.
[109] Leonidas J. Guibas,et al. The Earth Mover's Distance as a Metric for Image Retrieval , 2000, International Journal of Computer Vision.
[110] Leo Liberti. ON A CLASS OF NONCONVEX PROBLEMS WHERE ALL LOCAL MINIMA ARE GLOBAL , 2004 .
[111] Silvere Bonnabel,et al. Riemannian Metric and Geometric Mean for Positive Semidefinite Matrices of Fixed Rank , 2008, SIAM J. Matrix Anal. Appl..
[112] Jeffrey A. Fessler,et al. An Expanded Theoretical Treatment of Iteration-Dependent Majorize-Minimize Algorithms , 2007, IEEE Transactions on Image Processing.
[113] Giuseppe Caire,et al. Non-Bayesian Activity Detection, Large-Scale Fading Coefficient Estimation, and Unsourced Random Access with a Massive MIMO Receiver , 2019, ArXiv.
[114] Bertrand Thirion,et al. Multi-subject MEG/EEG source imaging with sparse multi-task regression , 2019, NeuroImage.
[115] Heinz H. Bauschke,et al. Fenchel–Rockafellar Duality , 2011 .
[116] J. Fermaglich. Electric Fields of the Brain: The Neurophysics of EEG , 1982 .
[117] A. Ben-Tal. On generalized means and generalized convex functions , 1977 .
[118] New York Dover,et al. ON THE CONVERGENCE PROPERTIES OF THE EM ALGORITHM , 1983 .
[119] 丸山 徹. Convex Analysisの二,三の進展について , 1977 .
[120] Stephen P. Boyd,et al. Variations and extension of the convex–concave procedure , 2016 .
[121] Klaus-Robert Müller,et al. Introduction to machine learning for brain imaging , 2011, NeuroImage.
[122] Stefan Haufe,et al. Large-scale EEG/MEG source localization with spatial flexibility , 2011, NeuroImage.
[123] Babak Hassibi,et al. Error bounds for Bregman denoising and structured natural parameter estimation , 2017, 2017 IEEE International Symposium on Information Theory (ISIT).
[124] Stefan Haufe,et al. Single-trial analysis and classification of ERP components — A tutorial , 2011, NeuroImage.
[125] David P. Wipf,et al. A unified Bayesian framework for MEG/EEG source imaging , 2009, NeuroImage.