Title :
Shapes of features and a modified measure for linear discriminant analysis
Author :
Ünsalan, Cem ; Ercil, A.
Author_Institution :
Ohio State Univ., Columbus, OH, USA
Abstract :
In this paper, the problem of selecting most representative features among a feature set is considered. Two new feature selection algorithms are introduced and their performances are compared with some well-known feature selection algorithms. The algorithms are tested with the iris data set, three artificially generated data sets and a data set obtained from steel surfaces
Keywords :
feature extraction; pattern classification; feature selection; feature shapes; iris data set; linear discriminant analysis measure; pattern classification; steel surfaces; Clustering algorithms; Feature extraction; Iris; Linear discriminant analysis; Low pass filters; Pattern recognition; Principal component analysis; Shape measurement; Steel; Testing;
Conference_Titel :
Pattern Recognition, 2000. Proceedings. 15th International Conference on
Conference_Location :
Barcelona
Print_ISBN :
0-7695-0750-6
DOI :
10.1109/ICPR.2000.906099
