NDLI: Optimum feature for high dimensional common-mean multiclass classification

Content Provider	IEEE Xplore Digital Library
Author	Hsieh, P.F. Lee, L.C.
Copyright Year	2001
Description	Author affiliation: Nat. Space Program Office, Hsinchu City, Taiwan (Hsieh, P.F.)
Abstract	Intense research efforts have contributed to feature extraction based on the discriminant information about class mean. Comparatively, the discriminant information about class covariance for the multiclass problem is not fully explored. In this study, we focus our efforts on this type of discriminant information about class covariance and limit the discussion to the multiclass common-mean case. We formulate the classification accuracy and obtain the following interesting results: (a) the classification accuracy can be expressed in terms of error functions in the univariate multiclass common-mean case. (b) For fixed largest and smallest class variances, the maximum classification accuracy occurs as all pairs of successive classes share the same ratio of class variance. An example of three classes with common class mean is given to show that the optimum feature is located in the direction in which the squared variance of class 2 is equal to the product of the variances of classes 1 and 3. The multiclass version of the Decision Boundary Feature Extraction method is applied to this example but cannot extract the optimum feature.
Starting Page	2799
Ending Page	2801
File Size	95890
Page Count	3
File Format	PDF
ISBN	0780370317
DOI	10.1109/IGARSS.2001.978167
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2001-07-09
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature extraction Data mining Hyperspectral imaging Aerospace industry Cities and towns Image analysis Covariance matrix Parameter estimation
Content Type	Text
Resource Type	Article

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