Supervised low rank matrix approximation for stable feature selection

Salem Alelyani, Huan Liu

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

Abstract

Increasing attention has been focused on the stability of selected features or selection stability, which is becoming a new measure in determining the effectiveness of a feature selection algorithm besides the learning performance. A recent study has shown that data characteristics play a significant role in selection stability. Hence, the solution to selection instability should begin with data. In this work, we propose a novel framework with a noise-reduction step before feature selection. Noise reduction is achieved via well-known low rank matrix approximation techniques (namely SVD and NMF) in a supervised manner to reduce data noise and variance between samples from the same class. The new framework is empirically shown to be highly effective with real high-dimensional datasets improving both selection stability and the precision of selecting relevant features while maintaining the classification accuracy for various feature selection methods.

Original languageEnglish (US)
Title of host publicationProceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012
Pages324-329
Number of pages6
DOIs
StatePublished - Dec 1 2012
Event11th IEEE International Conference on Machine Learning and Applications, ICMLA 2012 - Boca Raton, FL, United States
Duration: Dec 12 2012Dec 15 2012

Publication series

NameProceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012
Volume1

Other

Other11th IEEE International Conference on Machine Learning and Applications, ICMLA 2012
CountryUnited States
CityBoca Raton, FL
Period12/12/1212/15/12

Keywords

  • Low Rank Approximation
  • Noise Re-duction
  • selection algorithms
  • stability

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Education

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