NDLI: Gaussian Bayesian network structure learning strategies based on canonical correlation analysis

Content Provider	IEEE Xplore Digital Library
Author	Shuzhi Li Guanghua Xu Yongbao Feng
Copyright Year	2012
Description	Author affiliation: School of Mechanical Engineering Xi′ an Jiaotong University, Xi′ an Research Institute of Hi-Tech Xi′ an, Shaanxi Province, China (Shuzhi Li) \|\| Xi′ an Research Institute of Hi-Tech Xi′ an, Shaanxi Province, China (Yongbao Feng) \|\| School of Mechanical Engineering, Xi′ an Jiaotong University, State Key Laboratory for Manufacturing, Systems Engineering, Xi′ an Jiaotong University, Xi′ an, Shaanxi Province, China (Guanghua Xu)
Abstract	In order to solve the problem of low efficiency and low reliability of Gaussian Bayesian network structure learning methods, this paper proposes a new Gaussian Bayesian network structure learning algorithm from data based on the canonical correlation analysis. Firstly, by canonical correlation analysis of the son node and the candidate parent nodes, the correlation coefficients and correlation variables are given. Because the correlation coefficient indicates the association strength of family structure, we use correlation coefficient as measures of the family structure. Secondly, a new algorithm to establish parent nodes based on correlation variables is introduced. According to the correlation vectors to calculate the contribution value of candidate parent nodes, the contribution value is used to evaluate the association strength of parent node to son node. These nodes with the bigger contribution value are considered as the father nodes. Finally, the Bayesian network structure learning strategies is given based on canonical correlation analysis. The experimental results on the simulation standard data sets show that the new algorithm is effective and reliable.
Starting Page	156
Ending Page	161
File Size	144351
Page Count	6
File Format	PDF
ISBN	9781467312752
e-ISBN	9781467312783
DOI	10.1109/ICMA.2012.6282824
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-08-05
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Correlation Bayesian methods Algorithm design and analysis Vectors Probability distribution Covariance matrix structure learning strategies Gaussian Bayesian networks canonical correlation analysis
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Variational Bayesian Approach to Canonical Correlation Analysis

The Research on Two-Dimensional Gaussian Graphic Algorithm Based on Random Vectors Simulation Method

Canonical Correlation Analysis for Detecting Changes in Network Structure

An incremental structure learning approach for Bayesian Network

A gradient-based algorithm competitive with variational Bayesian EM for mixture of Gaussians

Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields

A fast dynamic Bayesian network algorithm for structure learning using a time-lagged partial correlation matrix

Bayesian Multi-Task Relationship Learning with Link Structure

Efficient covariance matrix methods for bayesian gaussian processes and hopfield neural networks (1999).

Gaussian Bayesian network structure learning strategies based on canonical correlation analysis

Similar Documents

Variational Bayesian Approach to Canonical Correlation Analysis

The Research on Two-Dimensional Gaussian Graphic Algorithm Based on Random Vectors Simulation Method

Canonical Correlation Analysis for Detecting Changes in Network Structure

An incremental structure learning approach for Bayesian Network

A gradient-based algorithm competitive with variational Bayesian EM for mixture of Gaussians

Efficient Bayesian spatial prediction with mobile sensor networks using Gaussian Markov random fields

A fast dynamic Bayesian network algorithm for structure learning using a time-lagged partial correlation matrix

Bayesian Multi-Task Relationship Learning with Link Structure

Efficient covariance matrix methods for bayesian gaussian processes and hopfield neural networks (1999).

Gaussian Bayesian network structure learning strategies based on canonical correlation analysis