TY - GEN
T1 - Feature subset selection with applications to hyperspectral data
AU - Chen, Hao
AU - Varshney, Pramod K.
PY - 2005
Y1 - 2005
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=33646816771&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33646816771&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2005.1415388
DO - 10.1109/ICASSP.2005.1415388
M3 - Conference contribution
AN - SCOPUS:33646816771
SN - 0780388747
SN - 9780780388741
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - II249-II252
BT - 2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05 - Proceedings - Image and Multidimensional Signal Processing Multimedia Signal Processing
T2 - 2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05
Y2 - 18 March 2005 through 23 March 2005
ER -