Directed Network Analysis Using Transfer Entropy Component Analysis

2018 ◽  
pp. 491-500
Author(s):  
Meihong Wu ◽  
Yangbin Zeng ◽  
Zhihong Zhang ◽  
Haiyun Hong ◽  
Zhuobin Xu ◽  
...  
Keyword(s):  
Network Analysis ◽  
Component Analysis ◽  
Transfer Entropy ◽  
Directed Network ◽  
Entropy Component

scholarly journals Erratum to “Network analysis using entropy component analysis”

2017 ◽  
Vol 6 (5) ◽  
pp. 831-831
Author(s):  
Cheng Ye ◽  
Richard C Wilson ◽  
Edwin R Hancock
Keyword(s):  
Network Analysis ◽  
Component Analysis ◽  
Entropy Component

scholarly journals Network analysis using entropy component analysis

2017 ◽  
Vol 6 (3) ◽  
pp. 404-429 ◽  
Author(s):  
Cheng Ye ◽  
Richard C Wilson ◽  
Edwin R Hancock
Keyword(s):  
Network Analysis ◽  
Component Analysis ◽  
Entropy Component

Connectivity and Centrality Characteristics of the Epileptogenic Focus Using Directed Network Analysis

2019 ◽  
Vol 27 (1) ◽  
pp. 22-30 ◽  
Author(s):  
Joshua A. Adkinson ◽  
Bharat Karumuri ◽  
Timothy N. Hutson ◽  
Rui Liu ◽  
Omar Alamoudi ◽  
...  
Keyword(s):  
Network Analysis ◽  
Directed Network ◽  
Epileptogenic Focus

A directed network analysis of the cardiome identifies molecular pathways contributing to the development of HFpEF

2020 ◽  
Vol 144 ◽  
pp. 66-75
Author(s):  
Georg Summer ◽  
Annika R. Kuhn ◽  
Chantal Munts ◽  
Daniela Miranda-Silva ◽  
Adelino F. Leite-Moreira ◽  
...  
Keyword(s):  
Network Analysis ◽  
Directed Network ◽  
Molecular Pathways

Class-information–incorporated kernel entropy component analysis with application to bearing fault diagnosis

2020 ◽  
pp. 107754632093203
Author(s):  
Hongdi Zhou ◽  
Fei Zhong ◽  
Tielin Shi ◽  
Wuxing Lai ◽  
Jian Duan ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Frequency Domain ◽  
Hebbian Learning ◽  
Component Analysis ◽  
Operating Conditions ◽  
Time Frequency ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Class Information ◽  
Entropy Component

Rolling bearings are present ubiquitously in industrial fields; timely fault diagnosis is of crucial significance in avoiding serious catastrophe. The extraction of ideal fault feature is a challenging task in vibration-based bearing fault detection. In this article, a novel method called class-information–incorporated kernel entropy component analysis is proposed for bearing fault diagnosis. The method is developed based on the Hebbian learning theory of neural network and the kernel entropy component analysis which attempts to compress the most Renyi quadratic entropy of input dataset after dimension reduction and presents a good performance for nonlinear feature extraction. Class-information–incorporated kernel entropy component analysis can take advantage of the label information of training samples to guide dimensional reduction and still follow the same simple mathematical formulation as kernel entropy component analysis. The high-dimensional feature dataset including time-domain, frequency-domain, and time–frequency domain characteristic parameters is first derived from the vibration signals. Then, the intrinsic geometric features are extracted by class-information–incorporated kernel entropy component analysis, and a classification strategy based on fusion information is applied to recognize different operating conditions of bearings. The experimental results demonstrated the feasibility and effectiveness of the proposed method.

scholarly journals The dynamics of information-driven coordination phenomena: A transfer entropy analysis

2016 ◽  
Vol 2 (4) ◽  
pp. e1501158 ◽  
Author(s):  
Javier Borge-Holthoefer ◽  
Nicola Perra ◽  
Bruno Gonçalves ◽  
Sandra González-Bailón ◽  
Alex Arenas ◽  
...  
Keyword(s):  
Information Transfer ◽  
Social Systems ◽  
Transfer Entropy ◽  
System Level ◽  
Directed Network ◽  
Characteristic Time Scale ◽  
Entropy Analysis ◽  
Collective Phenomena ◽  
Social Phenomena ◽  
Social Events

Data from social media provide unprecedented opportunities to investigate the processes that govern the dynamics of collective social phenomena. We consider an information theoretical approach to define and measure the temporal and structural signatures typical of collective social events as they arise and gain prominence. We use the symbolic transfer entropy analysis of microblogging time series to extract directed networks of influence among geolocalized subunits in social systems. This methodology captures the emergence of system-level dynamics close to the onset of socially relevant collective phenomena. The framework is validated against a detailed empirical analysis of five case studies. In particular, we identify a change in the characteristic time scale of the information transfer that flags the onset of information-driven collective phenomena. Furthermore, our approach identifies an order-disorder transition in the directed network of influence between social subunits. In the absence of clear exogenous driving, social collective phenomena can be represented as endogenously driven structural transitions of the information transfer network. This study provides results that can help define models and predictive algorithms for the analysis of societal events based on open source data.

scholarly journals Kernel Entropy Component Analysis for Remote Sensing Image Clustering

2012 ◽  
Vol 9 (2) ◽  
pp. 312-316 ◽  
Author(s):  
Luis Gomez-Chova ◽  
Robert Jenssen ◽  
Gustavo Camps-Valls
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Image ◽  
Component Analysis ◽  
Image Clustering ◽  
Entropy Component
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