General entropy criteria for inverse problems, with applications to data compression, pattern classification, and cluster analysis

Author

Jones, Lee K. ; Byrne, Charles L.

Author_Institution

Dept. of Math., Lowell Univ., MA, USA

Volume

36

Issue

1

fYear

1990

fDate

1/1/1990 12:00:00 AM

Firstpage

23

Lastpage

30

Abstract

Minimum distance approaches are considered for the reconstruction of a real function from finitely many linear functional values. An optimal class of distances satisfying an orthogonality condition analogous to that enjoyed by linear projections in Hilbert space is derived. These optimal distances are related to measures of distances between probability distributions recently introduced by C.R. Rao and T.K. Nayak (1985) and possess the geometric properties of cross entropy useful in speech and image compression, pattern classification, and cluster analysis. Several examples from spectrum estimation and image processing are discussed

Keywords

data compression; entropy; inverse problems; pattern recognition; picture processing; spectral analysis; speech analysis and processing; cluster analysis; cross entropy; data compression; general entropy criteria; image compression; image processing; inverse problems; minimum distance approach; pattern classification; probability distributions; spectrum estimation; speech compression; Entropy; Hilbert space; Image analysis; Image coding; Image reconstruction; Inverse problems; Pattern analysis; Pattern classification; Probability distribution; Speech analysis;

fLanguage

English

Journal_Title

Information Theory, IEEE Transactions on

Publisher

ieee

ISSN

0018-9448

Type

jour

DOI

10.1109/18.50370

Filename

50370