Contribution to Transfer Entropy Estimation via the k-Nearest-Neighbors Approach

Entropy

Jie Zhu¹  Jean-Jacques Bellanger¹  Huazhong Shu²  Régine Le Bouquin Jeannès¹ 

[1] Institut National de la Santé Et de la Recherche Médicale (INSERM), U 1099, Rennes F-35000, France; E-Mails:;Centre de Recherche en Information Biomédicale sino-français (CRIBs), Rennes F-35000, France

关键词: entropy estimation;    k nearest neighbors;    transfer entropy;    bias reduction;   

DOI  :  10.3390/e17064173

来源: mdpi

【 摘 要 】

This paper deals with the estimation of transfer entropy based on the k-nearest neighbors (k-NN) method. To this end, we first investigate the estimation of Shannon entropy involving a rectangular neighboring region, as suggested in already existing literature, and develop two kinds of entropy estimators. Then, applying the widely-used error cancellation approach to these entropy estimators, we propose two novel transfer entropy estimators, implying no extra computational cost compared to existing similar k-NN algorithms. Experimental simulations allow the comparison of the new estimators with the transfer entropy estimator available in free toolboxes, corresponding to two different extensions to the transfer entropy estimation of the Kraskov–Stögbauer–Grassberger (KSG) mutual information estimator and prove the effectiveness of these new estimators.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland