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Title:Network inference via clustered fused graphical lasso
Author(s):Zhu, Yizhi
Advisor(s):Koyejo, Oluwasanmi
Department / Program:Computer Science
Discipline:Computer Science
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Network Inference
Time Series
Graphical Lasso
Abstract:Estimating the dynamic connectivity structure among a system of entities has garnered much attention in recent years. While usual methods are designed to take advantage of temporal consistency to overcome noise, they conflict with the detectability of anomalies. We propose Clustered Fused Graphical Lasso (CFGL), a method using precomputed clustering information to improve the signal detectability as compared to typical Fused Graphical Lasso methods. We evaluate our method in both simulated and real-world datasets and conclude that, in many cases, CFGL can significantly improve the sensitivity to signals without a significant negative effect on the temporal consistency
Issue Date:2018-04-26
Rights Information:Copyright 2018 Yizhi Zhu
Date Available in IDEALS:2018-09-04
Date Deposited:2018-05

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