Efficient neighborhood selection for walk summable Gaussian graphical models

Yingxang Yang; Jalal Etesami; Negar Kiyavash

doi:10.1109/ACSSC.2017.8335180

Efficient neighborhood selection for walk summable Gaussian graphical models

Yingxang Yang, Jalal Etesami, Negar Kiyavash

University of Illinois at Urbana-Champaign

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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 language	English
Title of host publication	Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017
Editors	Michael B. Matthews
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	263-267
Number of pages	5
ISBN (Electronic)	9781538618233
DOIs	https://doi.org/10.1109/ACSSC.2017.8335180
State	Published - 2 Jul 2017
Externally published	Yes
Event	51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017 - Pacific Grove, United States Duration: 29 Oct 2017 → 1 Nov 2017

Publication series

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

Conference

Conference	51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017
Country/Territory	United States
City	Pacific Grove
Period	29/10/17 → 1/11/17

Access to Document

10.1109/ACSSC.2017.8335180

Cite this

Yang, Y., Etesami, J., & Kiyavash, N. (2017). 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). Article 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

Yang, Yingxang ; Etesami, Jalal ; Kiyavash, Negar. / Efficient neighborhood selection for walk summable Gaussian graphical models. Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017. editor / Michael B. Matthews. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 263-267 (Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017).

@inproceedings{b8eb49f9bd0744cf958b86db21329596,

title = "Efficient neighborhood selection for walk summable Gaussian graphical models",

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.",

author = "Yingxang Yang and Jalal Etesami and Negar Kiyavash",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017 ; Conference date: 29-10-2017 Through 01-11-2017",

year = "2017",

month = jul,

day = "2",

doi = "10.1109/ACSSC.2017.8335180",

language = "English",

series = "Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "263--267",

editor = "Matthews, {Michael B.}",

booktitle = "Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017",

}

Yang, Y, Etesami, J & Kiyavash, N 2017, Efficient neighborhood selection for walk summable Gaussian graphical models. in MB Matthews (ed.), Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017., 8335180, Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017, vol. 2017-October, Institute of Electrical and Electronics Engineers Inc., pp. 263-267, 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017, Pacific Grove, United States, 29/10/17. https://doi.org/10.1109/ACSSC.2017.8335180

Efficient neighborhood selection for walk summable Gaussian graphical models. / Yang, Yingxang; Etesami, Jalal; Kiyavash, Negar.
Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017. ed. / Michael B. Matthews. Institute of Electrical and Electronics Engineers Inc., 2017. p. 263-267 8335180 (Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017; Vol. 2017-October).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Efficient neighborhood selection for walk summable Gaussian graphical models

AU - Yang, Yingxang

AU - Etesami, Jalal

AU - Kiyavash, Negar

PY - 2017/7/2

Y1 - 2017/7/2

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85050983256&partnerID=8YFLogxK

U2 - 10.1109/ACSSC.2017.8335180

DO - 10.1109/ACSSC.2017.8335180

M3 - Conference contribution

AN - SCOPUS:85050983256

T3 - Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017

SP - 263

EP - 267

BT - Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017

A2 - Matthews, Michael B.

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017

Y2 - 29 October 2017 through 1 November 2017

ER -

Yang Y, Etesami J, Kiyavash N. Efficient neighborhood selection for walk summable Gaussian graphical models. In Matthews MB, editor, Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 263-267. 8335180. (Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017). doi: 10.1109/ACSSC.2017.8335180

Efficient neighborhood selection for walk summable Gaussian graphical models

Abstract

Publication series

Conference

Access to Document

Other files and links

Fingerprint

Cite this