Efficient neighborhood selection for walk summable Gaussian graphical models

Yingxang Yang, Jalal Etesami, Negar Kiyavash

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper addresses the problem of learning Gaussian graphical models using a threshold-based greedy neighborhood selection and pruning algorithm. The algorithm leverages the fact that the maximum conditional covariance between a node and its undiscovered neighbors given any estimated neighborhood is always bounded away from zero. We provide theoretical guarantees for the efficiency and accuracy of our algorithm for the class of walk summable Gaussian graphical models. We verify the performance of the algorithm through simulations.

Original languageEnglish (US)
Title of host publicationConference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017
EditorsMichael B. Matthews
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages263-267
Number of pages5
ISBN (Electronic)9781538618233
DOIs
StatePublished - Apr 10 2018
Event51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017 - Pacific Grove, United States
Duration: Oct 29 2017Nov 1 2017

Publication series

NameConference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017
Volume2017-October

Other

Other51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017
CountryUnited States
CityPacific Grove
Period10/29/1711/1/17

ASJC Scopus subject areas

  • Control and Optimization
  • Computer Networks and Communications
  • Hardware and Architecture
  • Signal Processing
  • Biomedical Engineering
  • Instrumentation

Fingerprint Dive into the research topics of 'Efficient neighborhood selection for walk summable Gaussian graphical models'. Together they form a unique fingerprint.

  • Cite this

    Yang, Y., Etesami, J., & Kiyavash, N. (2018). Efficient neighborhood selection for walk summable Gaussian graphical models. In M. B. Matthews (Ed.), Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017 (pp. 263-267). [8335180] (Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017; Vol. 2017-October). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ACSSC.2017.8335180