Network inference via clustered fused graphical lasso
Zhu, Yizhi
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https://hdl.handle.net/2142/101223
Description
Title
Network inference via clustered fused graphical lasso
Author(s)
Zhu, Yizhi
Issue Date
2018-04-26
Director of Research (if dissertation) or Advisor (if thesis)
Koyejo, Oluwasanmi
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(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
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