Feature subset selection with applications to hyperspectral data

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

2 Scopus citations

Abstract

Electrical Engineering and Computer Science Department Syracuse University, Syracuse, NY, 13244 Feature subset selection is very important in high dimensional datasets such as hyperspectral images. In this paper, we define a new feature redundancy measure. Two different feature selection algorithms are proposed based on this measure. Experimental results on a real hyperspectral dataset are presented to demonstrate the effectiveness of our methodology.

Original languageEnglish (US)
Title of host publication2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05 - Proceedings - Image and Multidimensional Signal Processing Multimedia Signal Processing
PagesII249-II252
DOIs
StatePublished - Dec 1 2005
Event2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05 - Philadelphia, PA, United States
Duration: Mar 18 2005Mar 23 2005

Publication series

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

Other

Other2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05
CountryUnited States
CityPhiladelphia, PA
Period3/18/053/23/05

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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    Chen, H., & Varshney, P. K. (2005). Feature subset selection with applications to hyperspectral data. In 2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05 - Proceedings - Image and Multidimensional Signal Processing Multimedia Signal Processing (pp. II249-II252). [1415388] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. II). https://doi.org/10.1109/ICASSP.2005.1415388