Authors:
Faycel El Ayeb
and
Faouzi Ghorbel
Affiliation:
La Manouba University, Tunisia
Keyword(s):
Dimensionality Reduction, Feature Extraction, Fisher Criterion, Orthogonal Density Estimation, Patrick-Fisher Distance, Smoothing Parameter, Handwritten Digits Classification, Invariant Descriptors.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Classification
;
Feature Selection and Extraction
;
Pattern Recognition
;
Shape Representation
;
Software Engineering
;
Theory and Methods
Abstract:
Here, we intend to give a rule for the choice of the smoothing parameter of the orthogonal estimate of Patrick-Fisher distance in the sense of the Mean Integrate Square Error. The orthogonal series density estimate precision depends strongly on the choice of such parameter which corresponds to the number of terms in the series expansion used. By using series of random simulations, we illustrate the better performance of its dimensionality reduction in the mean of the misclassification rate. We show also its better behavior for real data. Different invariant shape descriptors describing handwritten digits are extracted from a large database. It serves to compare the proposed adjusted Patrick-Fisher distance estimator with a conventional feature selection method in the mean of the probability error of classification.