DocumentCode :
912804
Title :
Nonparametric feature selection
Author :
Patrick, Edward A. ; Fischer, Frederic P., II
Volume :
15
Issue :
5
fYear :
1969
fDate :
9/1/1969 12:00:00 AM
Firstpage :
577
Lastpage :
584
Abstract :
Two groups of 
-dimensional observations of size 
and 
are known to be random vector variables from two unknown probability distribution functions [1]. A method is discussed for obtaining an 
-dimensional linear subspace of the observation space in which the -variate marginal distributions are most separated, based on a nonparametric estimate of probability density functions and a distance criterion. The distance used essentially is the 
norm of the difference between Parzen estimates of the two densities. An algorithm is developed that determines the subspace for which the distance between the two densities is maximized. Computer simulations are performed.
-dimensional observations of size and are known to be random vector variables from two unknown probability distribution functions [1]. A method is discussed for obtaining an -dimensional linear subspace of the observation space in which the -variate marginal distributions are most separated, based on a nonparametric estimate of probability density functions and a distance criterion. The distance used essentially is the norm of the difference between Parzen estimates of the two densities. An algorithm is developed that determines the subspace for which the distance between the two densities is maximized. Computer simulations are performed.Keywords :
Feature extraction; Nonparametric estimation; Covariance matrix; Density functional theory; Density measurement; Helium; Marine vehicles; Probability density function; Probability distribution; Vectors;
fLanguage :
English
Journal_Title :
Information Theory, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9448
Type :
jour
DOI :
10.1109/TIT.1969.1054354
Filename :
1054354
Link To Document :
