Correction to: Bayesian Topology Learning and noise removal from network data

[1]  Tharam S. Dillon,et al.  Noise Removal in the Presence of Significant Anomalies for Industrial IoT Sensor Data in Manufacturing , 2020, IEEE Internet of Things Journal.

[2]  Zhi Ding,et al.  Introducing Hypergraph Signal Processing: Theoretical Foundation and Practical Applications , 2019, IEEE Internet of Things Journal.

[3]  Daniel Pérez Palomar,et al.  Structured Graph Learning Via Laplacian Spectral Constraints , 2019, NeurIPS.

[4]  Karl Skretting,et al.  Topology Inference and Signal Representation Using Dictionary Learning , 2019, 2019 27th European Signal Processing Conference (EUSIPCO).

[5]  Karl Skretting,et al.  Robust Graph Topology Learning and Application in Stock Market Inference , 2019, 2019 IEEE International Conference on Signal and Image Processing Applications (ICSIPA).

[6]  H. Vincent Poor,et al.  Graph Topology Learning and Signal Recovery Via Bayesian Inference , 2019, 2019 IEEE Data Science Workshop (DSW).

[7]  Santiago Segarra,et al.  Connecting the Dots: Identifying Network Structure via Graph Signal Processing , 2018, IEEE Signal Processing Magazine.

[8]  Sergio Barbarossa,et al.  Graph Topology Inference Based on Sparsifying Transform Learning , 2018, IEEE Transactions on Signal Processing.

[9]  Pascal Frossard,et al.  Learning Graphs From Data: A Signal Representation Perspective , 2018, IEEE Signal Processing Magazine.

[10]  Eduardo Pavez,et al.  Graph Learning From Filtered Signals: Graph System and Diffusion Kernel Identification , 2018, IEEE Transactions on Signal and Information Processing over Networks.

[11]  Mahmoud Ramezani-Mayiami.  Joint Graph Learning and Signal Recovery via Kalman Filter for Multivariate Auto-Regressive Processes , 2018, 2018 26th European Signal Processing Conference (EUSIPCO).

[12]  Mahmoud Ramezani-Mayiami.  JOINT TOPOLOGY LEARNING AND GRAPH SIGNAL RECOVERY VIA KALMAN FILTER IN CAUSAL DATA PROCESSES , 2018, 2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP).

[13]  Georgios B. Giannakis,et al.  Topology Identification and Learning over Graphs: Accounting for Nonlinearities and Dynamics , 2018, Proceedings of the IEEE.

[14]  Mona Azadkia.  Adaptive Estimation of Noise Variance and Matrix Estimation via USVT Algorithm , 2018, 1801.10015.

[15]  Pierre Vandergheynst,et al.  Graph Signal Processing: Overview, Challenges, and Applications , 2017, Proceedings of the IEEE.

[16]  Eduardo Pavez,et al.  Learning Graphs With Monotone Topology Properties and Multiple Connected Components , 2017, IEEE Transactions on Signal Processing.

[17]  Michael G. Rabbat,et al.  Characterization and Inference of Graph Diffusion Processes From Observations of Stationary Signals , 2016, IEEE Transactions on Signal and Information Processing over Networks.

[18]  Baltasar Beferull-Lozano,et al.  Graph recursive least squares filter for topology inference in causal data processes , 2017, 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP).

[19]  Jelena Kovacevic,et al.  Graph topology recovery for regular and irregular graphs , 2017, 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP).

[20]  Georgios B. Giannakis,et al.  Network topology inference via elastic net structural equation models , 2017, 2017 25th European Signal Processing Conference (EUSIPCO).

[21]  José M. F. Moura,et al.  Signal Processing on Graphs: Causal Modeling of Unstructured Data , 2015, IEEE Transactions on Signal Processing.

[22]  Santiago Segarra,et al.  Network topology inference from non-stationary graph signals , 2017, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[23]  Pascal Frossard,et al.  Learning time varying graphs , 2017, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[24]  Antonio Ortega,et al.  Graph Learning From Data Under Laplacian and Structural Constraints , 2016, IEEE Journal of Selected Topics in Signal Processing.

[25]  Pascal Frossard,et al.  Learning Heat Diffusion Graphs , 2016, IEEE Transactions on Signal and Information Processing over Networks.

[26]  Alfred O. Hero,et al.  Learning sparse graphs under smoothness prior , 2016, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[27]  Santiago Segarra,et al.  Network Topology Inference from Spectral Templates , 2016, IEEE Transactions on Signal and Information Processing over Networks.

[28]  Georgios B. Giannakis,et al.  Tracking Switched Dynamic Network Topologies From Information Cascades , 2016, IEEE Transactions on Signal Processing.

[29]  Georgios B. Giannakis,et al.  Kernel-Based Structural Equation Models for Topology Identification of Directed Networks , 2016, IEEE Transactions on Signal Processing.

[30]  Hyunjoong Kim,et al.  Functional Analysis I , 2017 .

[31]  G. Giannakis,et al.  Nonlinear Structural Vector Autoregressive Models for Inferring Effective Brain Network Connectivity , 2016, 1610.06551.

[32]  Ramkrishna Pasumarthy,et al.  Identifying Topology of Power Distribution Networks Based on Smart Meter Data , 2016, ArXiv.

[33]  Michael Elad,et al.  Dual Graph Regularized Dictionary Learning , 2016, IEEE Transactions on Signal and Information Processing over Networks.

[34]  Antonio Ortega,et al.  Generalized Laplacian precision matrix estimation for graph signal processing , 2016, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[35]  Vassilis Kalofolias,et al.  How to Learn a Graph from Smooth Signals , 2016, AISTATS.

[36]  Pascal Frossard,et al.  Learning Laplacian Matrix in Smooth Graph Signal Representations , 2014, IEEE Transactions on Signal Processing.

[37]  Quanzheng Li,et al.  A Spectral Graph Regression Model for Learning Brain Connectivity of Alzheimer’s Disease , 2015, PloS one.

[38]  Lida Xu,et al.  The internet of things: a survey , 2014, Information Systems Frontiers.

[39]  Pierre Vandergheynst,et al.  GSPBOX: A toolbox for signal processing on graphs , 2014, ArXiv.

[40]  José M. F. Moura,et al.  Signal inpainting on graphs via total variation minimization , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[41]  José M. F. Moura,et al.  Signal denoising on graphs via graph filtering , 2014, 2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP).

[42]  Stephen P. Boyd,et al.  Proximal Algorithms , 2013, Found. Trends Optim..

[43]  Pascal Frossard,et al.  The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains , 2012, IEEE Signal Processing Magazine.

[44]  Peter D. Wentzell,et al.  Measurement errors in multivariate chemical data , 2013 .

[45]  Ieee Staff,et al.  2013 IEEE Global Conference on Signal and Information Processing (GlobalSIP) , 2013 .

[46]  Robert D. Nowak,et al.  Causal Network Inference Via Group Sparse Regularization , 2011, IEEE Transactions on Signal Processing.

[47]  Технология,et al.  National Climatic Data Center , 2011 .

[48]  Katya Scheinberg,et al.  Learning Sparse Gaussian Markov Networks Using a Greedy Coordinate Ascent Approach , 2010, ECML/PKDD.

[49]  Kim-Chuan Toh,et al.  Solving Log-Determinant Optimization Problems by a Newton-CG Primal Proximal Point Algorithm , 2010, SIAM J. Optim..

[50]  Joshua B. Tenenbaum,et al.  Discovering Structure by Learning Sparse Graphs , 2010 .

[51]  Lieven Vandenberghe,et al.  Topology Selection in Graphical Models of Autoregressive Processes , 2010, J. Mach. Learn. Res..

[52]  Mark Sanderson,et al.  Christopher D. Manning, Prabhakar Raghavan, Hinrich Schütze, Introduction to Information Retrieval, Cambridge University Press 2008. ISBN-13 978-0-521-86571-5, xxi + 482 pages , 2010, Natural Language Engineering.

[53]  Michael R. Lyu,et al.  Mining social networks using heat diffusion processes for marketing candidates selection , 2008, CIKM '08.

[54]  R. Tibshirani,et al.  Sparse inverse covariance estimation with the graphical lasso. , 2008, Biostatistics.

[55]  Alexandre d'Aspremont,et al.  Model Selection Through Sparse Max Likelihood Estimation Model Selection Through Sparse Maximum Likelihood Estimation for Multivariate Gaussian or Binary Data , 2022 .

[56]  M. Yuan,et al.  Model selection and estimation in the Gaussian graphical model , 2007 .

[57]  Gregory Dudek,et al.  A practical algorithm for network topology inference , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[58]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[59]  Hinrich Schütze,et al.  Introduction to information retrieval , 2008 .

[60]  Éric Gaussier,et al.  A Probabilistic Interpretation of Precision, Recall and F-Score, with Implication for Evaluation , 2005, ECIR.

[61]  Heinz H. Bauschke,et al.  Joint minimization with alternating Bregman proximity operators , 2005 .

[62]  Johan Löfberg,et al.  YALMIP : a toolbox for modeling and optimization in MATLAB , 2004 .

[63]  Johan Efberg,et al.  YALMIP : A toolbox for modeling and optimization in MATLAB , 2004 .

[64]  Rainer Goebel,et al.  Investigating directed cortical interactions in time-resolved fMRI data using vector autoregressive modeling and Granger causality mapping. , 2003, Magnetic resonance imaging.

[65]  Luiz A. Baccalá,et al.  Partial directed coherence: a new concept in neural structure determination , 2001, Biological Cybernetics.

[66]  Steven Kay,et al.  Fundamentals Of Statistical Signal Processing , 2001 .

[67]  J. E. Jackson,et al.  Statistical Factor Analysis and Related Methods: Theory and Applications , 1995 .

[68]  A. Basilevsky.  Statistical Factor Analysis and Related Methods: Theory and Applications , 1994 .

[69]  Karl J. Friston.  Functional and effective connectivity in neuroimaging: A synthesis , 1994 .

[70]  Nikolas P. Galatsanos,et al.  Methods for choosing the regularization parameter and estimating the noise variance in image restoration and their relation , 1992, IEEE Trans. Image Process..

[71]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[72]  D. Varberg,et al.  Another Proof that Convex Functions are Locally Lipschitz , 1974 .

[73]  J. Moreau.  Proximité et dualité dans un espace hilbertien , 1965 .

[74]  J. Neumann.  Zur Theorie der Gesellschaftsspiele , 1928 .