Joint Network Topology Inference via Structural Fusion Regularization

Author:

Yuan Yanli1ORCID,Soh De Wen2,Guo Kun3ORCID,Xiong Zehui2ORCID,Quek Tony Q. S.2ORCID

Affiliation:

1. School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing, China

2. Singapore University of Technology and Design, Singapore

3. Shanghai Key Laboratory of Multidimensional Information Processing, School of Communications and Electronics Engineering, East China Normal University, Shanghai, China

Funder

National Research Foundation

Info-communications Media Development Authority

Singapore University of Technology and Design

SUTD-ZJU IDEA

Ministry of Education - Singapore

SUTD Kickstarter Initiative

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computational Theory and Mathematics,Computer Science Applications,Information Systems

Reference54 articles.

1. Graph learning from filtered signals: Graph system and diffusion kernel identification;egilmez;IEEE Trans Signal Inf Process Netw,2018

2. Learning Graphs With Monotone Topology Properties and Multiple Connected Components

3. How to learn a graph from smooth signals;kalofolias;Proc Int Conf Artif Intell Statist,2016

4. Graph learning from data under Laplacian and structural constraints;eduardo;IEEE J Sel Topics Signal Process,2017

5. Graph theoretical analysis reveals disrupted topological properties of whole brain functional networks in temporal lobe epilepsy

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