LEARNING GAUSSIAN GRAPHICAL MODELS USING DISCRIMINATED HUB GRAPHICAL LASSO

被引：0

作者：

Li, Zhen ^{[1
]}

Bai, Jingtian ^{[1
]}

Zhou, Weilian ^{[1
]}

机构：

[1] North Carolina State Univ, Dept Stat, Raleigh, NC 27695 USA

来源：

2018 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) | 2018年

关键词：

Gaussian graphical model; precision matrix; graphical Lasso; discriminated hub graphical Lasso; prior information; SELECTION;

D O I：

暂无

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

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.

引用

页码：2471 / 2475

页数：5

共 50 条

[1] The cluster graphical lasso for improved estimation of Gaussian graphical models
Tan, Kean Ming
Witten, Daniela
Shojaie, Ali
[J]. COMPUTATIONAL STATISTICS & DATA ANALYSIS, 2015, 85 : 23 - 36
[2] Robust Gaussian Graphical Modeling with the Trimmed Graphical Lasso
Yang, Eunho
Lozano, Aurelie C.
[J]. ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 28 (NIPS 2015), 2015, 28
[3] Regularized Estimation of Piecewise Constant Gaussian Graphical Models: The Group-Fused Graphical Lasso
Gibberd, Alexander J.
Nelson, James D. B.
[J]. JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS, 2017, 26 (03) : 623 - 634
[4] Learning gene regulatory networks using gaussian process emulator and graphical LASSO
Chatrabgoun, H.
Soltanian, A. R.
Mahjub, H.
Bahreini, F.
[J]. JOURNAL OF BIOINFORMATICS AND COMPUTATIONAL BIOLOGY, 2021, 19 (03)
[5] Learning Gaussian graphical models with latent confounders
Wang, Ke
Franks, Alexander
Oh, Sang-Yun
[J]. JOURNAL OF MULTIVARIATE ANALYSIS, 2023, 198
[6] Singular Gaussian graphical models: Structure learning
Masmoudi, Khalil
Masmoudi, Afif
[J]. COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, 2018, 47 (10) : 3106 - 3117
[7] Learning Latent Variable Gaussian Graphical Models
Meng, Zhaoshi
Eriksson, Brian
Hero, Alfred O., III
[J]. INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 32 (CYCLE 2), 2014, 32 : 1269 - 1277
[8] Unsupervised Learning with Truncated Gaussian Graphical Models
Su, Qinliang
Liao, Xuejun
Li, Chunyuan
Gan, Zhe
Carin, Lawrence
[J]. THIRTY-FIRST AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, 2017, : 2583 - 2589
[9] Learning Dynamic Conditional Gaussian Graphical Models
Huang, Feihu
Chen, Songcan
[J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2018, 30 (04) : 703 - 716
[10] A note on the Lasso for Gaussian graphical model selection
Meinshausen, Nicolai
[J]. STATISTICS & PROBABILITY LETTERS, 2008, 78 (07) : 880 - 884

← 1 2 3 4 5 →