Title :
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
fDate :
1/1/1990 12:00:00 AM
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;
Journal_Title :
Information Theory, IEEE Transactions on
