A feature selection based on minimum upper bound of bayes error

Guorong Xuan, Zhenping Zhang, Peiqi Chai, Yun Q. Shi, Dongdong Fu

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

Abstract

This paper1 presents a novel feature selection scheme based on the upper bound of Bayes error under normal distribution for the multi-class dimension reduction problem. The upper bound of Bayes error in the multi-class problem is represented by the sum of the upper bound of Bayes error of every two-class pair. In order to obtain an accurate solution of the feature selection transform matrix in term of the minimum upper bound of Bayes error, a recursive algorithm based on gradient method is developed. The principal component analysis (PCA) is used as a pre-processing to reduce the intractably heavy computation burden of the recursive algorithm. The superior experimental results on the handwritten digit recognition with the MNIST database demonstrate the effectiveness of our proposed method.

Original languageEnglish (US)
Title of host publication2005 IEEE 7th Workshop on Multimedia Signal Processing, MMSP 2005
PublisherIEEE Computer Society
ISBN (Print)0780392892, 9780780392892
DOIs
StatePublished - Jan 1 2005
Event2005 IEEE 7th Workshop on Multimedia Signal Processing, MMSP 2005 - Shanghai, China
Duration: Oct 30 2005Nov 2 2005

Publication series

Name2005 IEEE 7th Workshop on Multimedia Signal Processing

Other

Other2005 IEEE 7th Workshop on Multimedia Signal Processing, MMSP 2005
Country/TerritoryChina
CityShanghai
Period10/30/0511/2/05

All Science Journal Classification (ASJC) codes

  • Signal Processing

Keywords

  • Fast feature selection based on minimum error bound (FFME)
  • Feature selection based on minimum error bound (FME)
  • Handwritten digit recognition
  • PCA pre-processing
  • Recursive algorithm

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