DocumentCode :
327665
Title :
A new robust quadratic discriminant function
Author :
Sakai, Mitsuru ; Yoneda, Masaaki ; Hase, Hiroyuki
Author_Institution :
Fac. of Eng., Toyama Univ., Japan
Volume :
1
fYear :
1998
fDate :
16-20 Aug 1998
Firstpage :
99
Abstract :
We propose a new quadratic discriminant function. It is devised based on the fact that eigenvalues of a sample covariance matrix are biased estimates of true eigenvalues. First, we rectify the biased eigenvalues. Then we construct a new covariance matrix whose eigenvalues are the rectified ones. Our quadratic discriminant function uses the covariance matrix. In a two-dimensional normal case, we show by a Monte Carlo method that our discriminant function works effectively, especially in the case of a small sample size
Keywords :
covariance matrices; eigenvalues and eigenfunctions; normal distribution; pattern recognition; Monte Carlo method; biased estimates; eigenvalues; robust quadratic discriminant function; sample covariance matrix; Covariance matrix; Eigenvalues and eigenfunctions; Estimation error; Gaussian distribution; Parameter estimation; Performance analysis; Robustness;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pattern Recognition, 1998. Proceedings. Fourteenth International Conference on
Conference_Location :
Brisbane, Qld.
ISSN :
1051-4651
Print_ISBN :
0-8186-8512-3
Type :
conf
DOI :
10.1109/ICPR.1998.711089
Filename :
711089
Link To Document :
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