L1-2 Regularized Logistic Regression

Jing Qin; Yifei Lou

doi:10.1109/IEEECONF44664.2019.9048830

L_1-2 Regularized Logistic Regression

Jing Qin, Yifei Lou

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

9 Scopus citations

Abstract

Logistic regression has become a fundamental tool to facilitate data analysis and prediction in a variety of applications, including health care and social sciences. Depending on different sparsity assumptions, logistic regression models often incorporate various regularizations, including ℓ₁-norm, ℓ₂-norm and some non-convex regularizations. In this paper, we propose a nonconvex ℓ_1-2-regularized logistic regression model assuming that the coefficients to be recovered are highly sparse. We derive two numerical algorithms with guaranteed convergence based on the alternating direction method of multipliers and the proximal operator of ℓ_1-2. Numerical experiments on real data demonstrate the great potential of the proposed approach.

Original language	English
Title of host publication	Conference Record - 53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019
Editors	Michael B. Matthews
Pages	779-783
Number of pages	5
ISBN (Electronic)	9781728143002
DOIs	https://doi.org/10.1109/IEEECONF44664.2019.9048830
State	Published - Nov 2019
Event	53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019 - Pacific Grove, United States Duration: Nov 3 2019 → Nov 6 2019

Publication series

Name	Conference Record - Asilomar Conference on Signals, Systems and Computers
Volume	2019-November
ISSN (Print)	1058-6393

Conference

Conference	53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019
Country/Territory	United States
City	Pacific Grove
Period	11/3/19 → 11/6/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

ASJC Scopus subject areas

Signal Processing
Computer Networks and Communications

Access to Document

10.1109/IEEECONF44664.2019.9048830

Cite this

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title = "L1-2 Regularized Logistic Regression",

abstract = "Logistic regression has become a fundamental tool to facilitate data analysis and prediction in a variety of applications, including health care and social sciences. Depending on different sparsity assumptions, logistic regression models often incorporate various regularizations, including ℓ1-norm, ℓ2-norm and some non-convex regularizations. In this paper, we propose a nonconvex ℓ1-2-regularized logistic regression model assuming that the coefficients to be recovered are highly sparse. We derive two numerical algorithms with guaranteed convergence based on the alternating direction method of multipliers and the proximal operator of ℓ1-2. Numerical experiments on real data demonstrate the great potential of the proposed approach.",

author = "Jing Qin and Yifei Lou",

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year = "2019",

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Qin, J & Lou, Y 2019, L_1-2 Regularized Logistic Regression. in MB Matthews (ed.), Conference Record - 53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019., 9048830, Conference Record - Asilomar Conference on Signals, Systems and Computers, vol. 2019-November, pp. 779-783, 53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019, Pacific Grove, United States, 11/3/19. https://doi.org/10.1109/IEEECONF44664.2019.9048830

L_1-2 Regularized Logistic Regression. / Qin, Jing; Lou, Yifei.
Conference Record - 53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019. ed. / Michael B. Matthews. 2019. p. 779-783 9048830 (Conference Record - Asilomar Conference on Signals, Systems and Computers; Vol. 2019-November).

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

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AB - Logistic regression has become a fundamental tool to facilitate data analysis and prediction in a variety of applications, including health care and social sciences. Depending on different sparsity assumptions, logistic regression models often incorporate various regularizations, including ℓ1-norm, ℓ2-norm and some non-convex regularizations. In this paper, we propose a nonconvex ℓ1-2-regularized logistic regression model assuming that the coefficients to be recovered are highly sparse. We derive two numerical algorithms with guaranteed convergence based on the alternating direction method of multipliers and the proximal operator of ℓ1-2. Numerical experiments on real data demonstrate the great potential of the proposed approach.

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