Fast lasso screening tests based on correlations

Zhen James Xiang, Peter Jeffrey Ramadge

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

50 Scopus citations

Abstract

Representing a vector as a sparse linear combination of codewords, e.g. by solving a lasso problem, lies at the heart of many machine learning and statistics applications. To improve the efficiency of solving lasso problems, we systematically investigate lasso screening, a process that quickly identifies dictionary entries that won't be used in the optimal sparse representation, and hence can be removed from the problem. We propose a general test called an R region test that unifies existing screening tests and we derive a particular instance called the dome test. This test is stronger than existing screening tests and can be executed in linear-time as a two-pass test with a memory footprint of only three codewords.

Original languageEnglish (US)
Title of host publication2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings
Pages2137-2140
Number of pages4
DOIs
StatePublished - Oct 23 2012
Event2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Kyoto, Japan
Duration: Mar 25 2012Mar 30 2012

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012
CountryJapan
CityKyoto
Period3/25/123/30/12

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

Keywords

  • Algorithms
  • Machine learning
  • Optimization

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