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Malgorzata Bogdan

Professor

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Structure learning of Gaussian Markov random fields with false discovery rate control

Författare

  • Sangkyun Lee
  • Piotr Sobczyk
  • Malgorzata Bogdan

Summary, in English

In this paper, we propose a new estimation procedure for discovering the structure of Gaussian Markov random fields (MRFs) with false discovery rate (FDR) control, making use of the sorted ℓ1-norm (SL1) regularization. A Gaussian MRF is an acyclic graph representing a multivariate Gaussian distribution, where nodes are random variables and edges represent the conditional dependence between the connected nodes. Since it is possible to learn the edge structure of Gaussian MRFs directly from data, Gaussian MRFs provide an excellent way to understand complex data by revealing the dependence structure among many inputs features, such as genes, sensors, users, documents, etc. In learning the graphical structure of Gaussian MRFs, it is desired to discover the actual edges of the underlying but unknown probabilistic graphical model-it becomes more complicated when the number of random variables (features) p increases, compared to the number of data points n. In particular, when p ≥ n, it is statistically unavoidable for any estimation procedure to include false edges. Therefore, there have been many trials to reduce the false detection of edges, in particular, using different types of regularization on the learning parameters. Our method makes use of the SL1 regularization, introduced recently for model selection in linear regression. We focus on the benefit of SL1 regularization that it can be used to control the FDR of detecting important random variables. Adapting SL1 for probabilistic graphical models, we show that SL1 can be used for the structure learning of Gaussian MRFs using our suggested procedure nsSLOPE (neighborhood selection Sorted L-One Penalized Estimation), controlling the FDR of detecting edges.

Publiceringsår

2019-10-01

Språk

Engelska

Publikation/Tidskrift/Serie

Symmetry

Volym

11

Issue

10

Dokumenttyp

Artikel i tidskrift

Förlag

MDPI AG

Ämne

  • Probability Theory and Statistics

Nyckelord

  • FDR control
  • Gaussian Markov random field
  • Inverse Covariance Matrix Estimation

Status

Published

ISBN/ISSN/Övrigt

  • ISSN: 2073-8994