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Correntropy based Granger causality

Il Park, Jose C. Principe
2008 Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing  
We propose a novel nonlinear extension to Granger causality.  ...  The method is demonstrated by detecting the direction of coupling in a chaotic system where the original Granger causality failed.  ...  The correntropy based Granger causality can detect the direction of coupling correctly if it is stronger than 0.3 while the classic Granger causality does not show significant deviation from the baseline  ... 
doi:10.1109/icassp.2008.4518432 dblp:conf/icassp/ParkP08 fatcat:5vz5vt4rhvbmha3wpusk73geny

Causal Identification Based on Compressive Sensing of Air Pollutants using Urban Big Data

Mingwei Li, Jinpeng Li, Shuangning Wan, Hao Chen, Chao Liu
2020 IEEE Access  
INDEX TERMS Granger causality analysis, maximum correntropy criterion, data compression, air pollutant.  ...  A novel compressive sensing causality analysis (CS-Causality) method, which combines Granger causality analysis (GCA) and maximum correntropy criterion (MCC), is presented for efficient identification  ...  Granger causality analysis (GCA) and compressive sensing (CS).  ... 
doi:10.1109/access.2020.3000767 fatcat:by5mteimsjgujm772c6eoxgzle

Kernel adaptive filtering with maximum correntropy criterion

Songlin Zhao, Badong Chen, Jose C. Principe
2011 The 2011 International Joint Conference on Neural Networks  
This fact motivates us in this paper to develop a new kernel adaptive algorithm, called the kernel maximum correntropy (KMC), which combines the advantages of the KLMS and maximum correntropy criterion  ...  Recently, the correntropy, as an alternative of MSE, has been successfully used in nonlinear and non-Gaussian signal processing and machine learning domains.  ...  Moreover, similar extension of Granger causality by correntropy can detect causality of a nonlinear dynamical system where the linear Granger causality failed [14] .  ... 
doi:10.1109/ijcnn.2011.6033473 dblp:conf/ijcnn/ZhaoCP11 fatcat:nagyamfknnad7jzmhrdhyh22ym

Connectivity measures applied to human brain electrophysiological data

R.E. Greenblatt, M.E. Pflieger, A.E. Ossadtchi
2012 Journal of Neuroscience Methods  
Correntropy-based Granger causality Correntropy (Santamaria et al., 2006) is a recently developed second order statistic that is well-adapted by virtue of computational efficiency for estimation of non-Gaussian  ...  An essentially similar and widely used operational definition of causality has been provided by Granger (1969) , and has come to be known as 'Granger causality'.  ... 
doi:10.1016/j.jneumeth.2012.02.025 pmid:22426415 pmcid:PMC5549799 fatcat:kkgtpebptne7jjuthft7bafyfq

The Assumption of Non-Gaussianity in Natural and Social Sciences and Its Influence on Detection of Causal Relationships [chapter]

Kateina Hlavkov-Schindler
2012 Theoretical and Methodological Approaches to Social Sciences and Knowledge Management  
Another non-linear extension of Granger causality is so called correntropy (Park & Principe, 2008) .  ...  The standard test of G-causality developed in (Granger, 1969) is based on a linear regression model. In the following we will define Granger causality by using the notation from (Barnett, 2009) .  ... 
doi:10.5772/38542 fatcat:whgor6oveba7bdmi2st7tww6aa

A comparative study of synchrony measures for the early diagnosis of Alzheimer's disease based on EEG

J. Dauwels, F. Vialatte, T. Musha, A. Cichocki
2010 NeuroImage  
Measures that are only weakly correlated with the correlation coefficient include the phase synchrony indices, Granger causality measures, and stochastic event synchrony measures.  ...  Mild cognitive impairment (MCI) Electroencephalography (EEG) Synchrony Correlation coefficient Coherence function Corr-entropy coefficient Coh-entropy coefficient Wav-entropy coefficient Granger causality  ...  The Granger causality measures are defined in terms of the matrices A, H, and S. We now list the most common Granger causality measures.  ... 
doi:10.1016/j.neuroimage.2009.06.056 pmid:19573607 fatcat:mbtzcotanbdazmrsfmrdgmd4vy

Multi-Model Variational Bayesian Approaches for Causality Analysis

Aswathi Prabhakaran
2021
Furthermore, Bayesian network based approach for Granger causality analysis in multi-mode systems can handle data with outliers.  ...  Granger causality analysis is one of the widely popular methods for causality analysis.  ...  This chapter provides a brief overview of the popular datadriven causality methods, namely coherence-based methods, entropy-based methods and Granger causality.  ... 
doi:10.7939/r3-wacj-t463 fatcat:rubixv2q6nf2xetwcc7tguwgqu

A Review of EEG Signal Features and their Application in Driver Drowsiness Detection Systems

Igor Stancin, Mario Cifrek, Alan Jovic
2021 Sensors  
reviewing the applications of EEG features and deep learning approaches in driver drowsiness detection, and finally we discuss the open challenges and opportunities in improving driver drowsiness detection based  ...  Granger causality Spectral Granger causality Phase slope index Number of vertices Number of edges Degree PSI D Coarse-grained entropy Mean degree Correntropy CoE Degree distribution Approximate entropy  ...  It is insensitive to the mixtures of the independent sources, which is the main problem for Granger causality.  ... 
doi:10.3390/s21113786 pmid:34070732 fatcat:2gyzb24wijazfdq5ak3xynooqi

A Comparative Study of Synchrony Measures for the Early Detection of Alzheimer's Disease Based on EEG [chapter]

Justin Dauwels, François Vialatte, Andrzej Cichocki
Lecture Notes in Computer Science  
In this paper, a wide variety of synchrony measures is investigated in the context of AD detection, including the cross-correlation coefficient, the mean-square and phase coherence function, Granger causality  ...  For the data set at hand, only two synchrony measures are able to convincingly distinguish MCI patients from age-matched control patients (p < 0.005), i.e., Granger causality (in particular, full-frequency  ...  Granger Causality Granger causality 1 refers to a family of synchrony measures that are derived from linear stochastic models of time series; as the above linear interdependence measures, they quantify  ... 
doi:10.1007/978-3-540-69158-7_13 fatcat:zuj53g5o7rf6tdmqyj52e5ui34

Aesthetic Highlight Detection in Movies Based on Synchronization of Spectators' Reactions

Michal Muszynski, Theodoros Kostoulas, Patrizia Lombardo, Thierry Pun, Guillaume Chanel
2018 ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)  
The results show that the unsupervised architecture relying on synchronization measures is able to capture different properties of spectators' synchronization and detect aesthetic highlights based on both  ...  Also, non-linear extensions of Granger causality have been proposed in [1, 13] . Several synchronization measures come from information theory [18] .  ...  Also, the Granger causality is considered as a part of synchronization measures that are derived from linear stochastic models of time series which extent linear dependencies between signals [6, 35] .  ... 
doi:10.1145/3175497 fatcat:732g2xixc5hvbahbx3r3t5wv6m

Unit commitment considering multiple charging and discharging scenarios of plug-in electric vehicles

Zhile Yang, Kang Li, Qun Niu, Aoife Foley
2015 2015 International Joint Conference on Neural Networks (IJCNN)  
, 10:50AM-12:10PM, Room: Mangerton, Chair: Cavalcanti, George 10:50AM Adaptive Skew-Sensitive Fusion of Ensembles and their Application to Face Re-Identification [#15234] Miguel De-la-Torre, Eric Granger  ...  Suykens P186 Probabilistic Dynamic Causal Model for Temporal Data [#15284] Xiabing Zhou, Wenhao Huang, Ni Zhang, Weisong Hu, Sizhen Du, Guojie Song and Kunqing Xie P187 Neural PID Adaptive Generator Excitation  ... 
doi:10.1109/ijcnn.2015.7280446 dblp:conf/ijcnn/YangLNF15 fatcat:6xlakikcfzfyhhm2spooe2j7ra

Multivariate Time Series Imputation by Graph Neural Networks [article]

Andrea Cini, Ivan Marisca, Cesare Alippi
2021 arXiv   pre-print
Notably, most of state-of-the-art imputation methods based on deep learning do not explicitly model relational aspects and, in any case, do not exploit processing frameworks able to adequately represent  ...  Acknowledgements This research is funded by the Swiss National Science Foundation project 200021_172671: "ALPSFORT: A Learning graPh-baSed framework FOr cybeR-physical sysTems."  ...  ., Pearson correlation or Granger causality) and/or that are close in a certain latent space.  ... 
arXiv:2108.00298v2 fatcat:623bsei7k5bdrnea4gyg6rbrsi

Multi-grid cellular genetic algorithm for optimizing variable ordering of ROBDDs

Cristian Rotaru, Octav Brudaru
2012 2012 IEEE Congress on Evolutionary Computation  
A similarity based communication protocol between clusters of individuals from parallel grids is defined. The exchange of genetic material proves to considerably boost the quality of the solution.  ...  Huang and Rua-Huan Tsaih, The Prediction Approach with Growing Hierarchical Self-Organizing Map 465, Rakesh Chalasani, Goktug Cinar and Jose Principe, Sequential Causal Estimation and Learning from Time-Varying  ...  Bouaynaya and Lakhmi Jain, Particle Filters and Beamforming for EEG Source Estimation Tuesday, Hybrid, TuH 3-2, 13:30-14:30, Computational Intelligence In Biometrics (Hybrid) 1, Eliza Yingzi Du, Eric Granger  ... 
doi:10.1109/cec.2012.6256590 dblp:conf/cec/RotaruB12 fatcat:4ly3nrktw5habc6lf5err7d5py

Designing, Developing and Validating a Forecasting Method for the Month Ahead Hourly Electricity Consumption in the Case of Medium Industrial Consumers

Dana-Mihaela Petroșanu
2019 Processes  
In terms of the methods, the authors apply "Granger causality" and "partial Grainger causality" networks using electricity consumption data regarding the industries of Guangdong, Guangxi, Guizhou, Yunnan  ...  causality and partial Grainger causality networks [13] , LSSVM enriched using a MCC [15] , while the current paper develops an approach based on the NARX along with LSTM artificial neural networks.  ... 
doi:10.3390/pr7050310 fatcat:tgoyqmohzngntdvedpdmnjvtda