@inproceedings{8b65cc4e6fb94edd991c4f0dc4ee2fde,
title = "Causality analysis based on matrix transfer entropy",
abstract = "Transfer Entropy (TE) is one of the most commonly used methods to detect the causal relationship between a pair of time series. However, the computational complexity of the TE is very hign, because its calculation needs to estimate the probability distribution of the variables. In order to solve this problem, we propose a new version of the TE based on the concept of Matrix Entropy (MT), called Matrix Transfer Entropy (MTE). MTE can be used for two variables with linear or non-linear causal relationships. Compared with the traditional TE, the new approach can achieve more robust results. Bypassing the estimation of the probability density functions (PDFs) of the variables, the computational complexity of the MTE is not high. Experimental results on two toy examples are provided to demonstrate the performance of the MTE. Additionally, the new method is applied to a real clinical dataset to analyze the cardiorespiratory causality.",
keywords = "Causality, Computational complexity, Matrix Entropy, Matrix Transfer Entropy, Transfer Entropy",
author = "Rongjin Ma and Badong Chen and Jianfeng Xiao and Jingli Shao",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 28th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2018 ; Conference date: 17-09-2018 Through 20-09-2018",
year = "2018",
month = oct,
day = "31",
doi = "10.1109/MLSP.2018.8517095",
language = "英语",
series = "IEEE International Workshop on Machine Learning for Signal Processing, MLSP",
publisher = "IEEE Computer Society",
editor = "Nelly Pustelnik and Zheng-Hua Tan and Zhanyu Ma and Jan Larsen",
booktitle = "2018 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2018 - Proceedings",
}