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LEARNING GAUSSIAN GRAPHICAL MODELS USING DISCRIMINATED HUB GRAPHICAL LASSO

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Citation Author(s):
Jingtian Bai
Zhen Li, Jingtian Bai, Weilian Zhou
Submitted by:
Jingtian Bai
Last updated:
15 April 2018 - 7:37pm
Document Type:
Poster
Document Year:
2018
Event:
ICASSP 2018
Presenters:
Zhen Li, Jingtian Bai
Paper Code:
MLSP-P2.5
Categories:
Graphical and kernel methods (MLR-GRKN)
 

We develop a new method called Discriminated Hub Graphical Lasso (DHGL) based on Hub Graphical Lasso (HGL) by providing the prior information of hubs. We apply this new method in two situations: with known hubs and without known hubs. Then we compare DHGL with HGL using several measures of performance. When some hubs are known, we can always estimate the precision matrix better via DHGL than HGL. When no hubs are known, we use Graphical Lasso (GL) to provide information of hubs and find that the performance of DHGL will always be better than HGL if correct prior information is given, and will rarely degenerate when the prior information is incorrect.

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