Feature Evalution with Measures of Probabilistic Dependence

Author

Vilmansen, Toomas R.

Author_Institution

Department of Electrical Engineering, University of British Columbia

Issue

4

fYear

1973

fDate

4/1/1973 12:00:00 AM

Firstpage

381

Lastpage

388

Abstract

In this paper, measures of probabilistic dependence are derived from distance measures and are applied to feature evaluation in pattern recognition. The main properties of the measures are derived and are discussed in their application to feature-class dependency. Relations between the measures and error probability are derived. Experiments using feature subsets extracted from Munson´s hand-printed data are performed to compare the feature-evaluating capabilities of the measures both relative to each other and relative to error probability.

Keywords

Bhattacharyya dependence, error probability, feature evaluation, Joshi´s dependence, Kolmogorov dependence, Matusita´s dependence, measures of probabilistic dependence, mutual information, pattern recognition.; Data mining; Entropy; Error probability; Feature extraction; Multidimensional systems; Mutual information; Pattern recognition; Performance evaluation; Random variables; Bhattacharyya dependence, error probability, feature evaluation, Joshi´s dependence, Kolmogorov dependence, Matusita´s dependence, measures of probabilistic dependence, mutual information, pattern recognition.;

fLanguage

English

Journal_Title

Computers, IEEE Transactions on

Publisher

ieee

ISSN

0018-9340

Type

jour

DOI

10.1109/T-C.1973.223725

Filename

1672318