A safe screening rule for sparse logistic regression

Jie Wang, Jiayu Zhou, Jun Liu, Peter Wonka, Jieping Ye

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

27 Citations (Scopus)

Abstract

The l<inf>1</inf>-regularized logistic regression (or sparse logistic regression) is a widely used method for simultaneous classification and feature selection. Although many recent efforts have been devoted to its efficient implementation, its application to high dimensional data still poses significant challenges. In this paper, we present a fast and effective sparse logistic regression screening rule (Slores) to identify the "0" components in the solution vector, which may lead to a substantial reduction in the number of features to be entered to the optimization. An appealing feature of Slores is that the data set needs to be scanned only once to run the screening and its computational cost is negligible compared to that of solving the sparse logistic regression problem. Moreover, Slores is independent of solvers for sparse logistic regression, thus Slores can be integrated with any existing solver to improve the efficiency. We have evaluated Slores using high-dimensional data sets from different applications. Experiments demonstrate that Slores outperforms the existing state-of-the-art screening rules and the efficiency of solving sparse logistic regression can be improved by one magnitude.

Original languageEnglish (US)
Title of host publicationAdvances in Neural Information Processing Systems
PublisherNeural information processing systems foundation
Pages1053-1061
Number of pages9
Volume2
EditionJanuary
StatePublished - 2014
Event28th Annual Conference on Neural Information Processing Systems 2014, NIPS 2014 - Montreal, Canada
Duration: Dec 8 2014Dec 13 2014

Other

Other28th Annual Conference on Neural Information Processing Systems 2014, NIPS 2014
CountryCanada
CityMontreal
Period12/8/1412/13/14

Fingerprint

Logistics
Screening
Feature extraction
Costs
Experiments

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

Cite this

Wang, J., Zhou, J., Liu, J., Wonka, P., & Ye, J. (2014). A safe screening rule for sparse logistic regression. In Advances in Neural Information Processing Systems (January ed., Vol. 2, pp. 1053-1061). Neural information processing systems foundation.

A safe screening rule for sparse logistic regression. / Wang, Jie; Zhou, Jiayu; Liu, Jun; Wonka, Peter; Ye, Jieping.

Advances in Neural Information Processing Systems. Vol. 2 January. ed. Neural information processing systems foundation, 2014. p. 1053-1061.

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

Wang, J, Zhou, J, Liu, J, Wonka, P & Ye, J 2014, A safe screening rule for sparse logistic regression. in Advances in Neural Information Processing Systems. January edn, vol. 2, Neural information processing systems foundation, pp. 1053-1061, 28th Annual Conference on Neural Information Processing Systems 2014, NIPS 2014, Montreal, Canada, 12/8/14.
Wang J, Zhou J, Liu J, Wonka P, Ye J. A safe screening rule for sparse logistic regression. In Advances in Neural Information Processing Systems. January ed. Vol. 2. Neural information processing systems foundation. 2014. p. 1053-1061
Wang, Jie ; Zhou, Jiayu ; Liu, Jun ; Wonka, Peter ; Ye, Jieping. / A safe screening rule for sparse logistic regression. Advances in Neural Information Processing Systems. Vol. 2 January. ed. Neural information processing systems foundation, 2014. pp. 1053-1061
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