DocumentCode :
326977
Title :
Linear feature extraction for multiclass problems
Author :
Hsieh, Pi-Fuei ; Landgrebe, David
Author_Institution :
Sch. of Electr. & Comput. Eng., Purdue Univ., West Lafayette, IN, USA
Volume :
4
fYear :
1998
fDate :
6-10 Jul 1998
Firstpage :
2050
Abstract :
Motivated by the need for a fast and effective feature extraction method for multiclass problems, a feature extraction method is developed to satisfy two requirements: (1) perform on a class-statistics basis (2) use discriminant information about covariance-difference as well as mean-difference. Experiments show that the new feature extraction method has fulfilled the requirements when the number of training samples is large. Experiments with a small number of training samples were also conducted for showing the limitation of feature extraction
Keywords :
feature extraction; geophysical signal processing; image classification; remote sensing; class-statistics basis; covariance-difference; discriminant information; feature extraction; linear feature extraction; mean-difference; multiclass problems; training samples; Data analysis; Data mining; Feature extraction; Gaussian distribution; Hyperspectral imaging; Information analysis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Geoscience and Remote Sensing Symposium Proceedings, 1998. IGARSS '98. 1998 IEEE International
Conference_Location :
Seattle, WA
Print_ISBN :
0-7803-4403-0
Type :
conf
DOI :
10.1109/IGARSS.1998.703737
Filename :
703737
Link To Document :
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