The 2-codeword screening test for lasso problems

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

2 Scopus citations

Abstract

Solving a lasso problem is a practical approach for acquiring a sparse representation of a signal with respect to a given dictionary. Driven by the demand for sparse representations over large-scale data in machine learning and statistics, we explore lasso screening tests. These enhance solution efficiency via the elimination of codewords absent in the optimal solution prior to detailed computation. On basis of the concept of a region test and the recently introduced dome test, we propose the 2-codeword test, which uses two codewords together in a correlation screening test. In addition to the rejection rate as the performance measure, we introduce an innovative way to access the performance of a screening test, called the uncertainty measure, via a comparison with the optimal test.

Original languageEnglish (US)
Title of host publication2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings
Pages3307-3311
Number of pages5
DOIs
StatePublished - Oct 18 2013
Event2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Vancouver, BC, Canada
Duration: May 26 2013May 31 2013

Publication series

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

Other

Other2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013
CountryCanada
CityVancouver, BC
Period5/26/135/31/13

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

Keywords

  • Algorithms
  • Machine learning
  • Optimization

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