An Algorithm for Optimal Single Linear Feature Extraction from Several Gaussian Pattern Classes

A computational algorithm is presented for the extraction of an optimal single linear feature from several Gaussian pattern classes. The algorithm minimizes the increase in the probability of misclassification in the transformed (feature) space. Numerical results on the application of this procedure...

Full description

Saved in:
Bibliographic Details
Main Authors: Starks,S A, deFigueiredo,R J P, Van Rooy,D L
Format: Report
Language:English
Subjects:
ALGORITHMS
BAYES THEOREM
CLASSIFICATION
CONVERGENCE
Euclidean space
GAUSSIAN QUADRATURE
LINEAR PROGRAMMING
PROBABILITY DENSITY FUNCTIONS
REMOTE DETECTORS
Remote sensing
Statistics and Probability
VECTOR SPACES
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:A computational algorithm is presented for the extraction of an optimal single linear feature from several Gaussian pattern classes. The algorithm minimizes the increase in the probability of misclassification in the transformed (feature) space. Numerical results on the application of this procedure to the remotely sensed data from the Purdue C1 flight line as well as LANDSAT data are presented. It was found that classification using the optimal single linear feature yielded a value for the probability of misclassification on the order of 30% less than that obtained by using the best single untransformed feature. Also, the optimal single linear feature gave performance results comparable to those obtained by using the two features which maximized the average divergence.