Thumbnail
Access Restriction
Subscribed

Author Ide, T.
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2005
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Computer programming, programs & data
Subject Keyword Gaussian distribution ♦ Probability density function ♦ Data mining ♦ Kernel ♦ Laboratories ♦ Covariance matrix ♦ Pattern recognition ♦ Taylor series
Abstract We propose a new theoretical framework for generalizing the traditional notion of covariance. First, we discuss the role of pairwise cross-cumulants by introducing a cluster expansion technique for the cumulant generating function. Next, we introduce a novel concept of symmetry decomposition of probability density functions according to the C/sub 4V/ group. By utilizing the irreducible representations, generalized covariances are explicitly defined, and their utility is demonstrated using an analytically solvable model.
Description Author affiliation: Tokyo Res. Lab., IBM Res., Kanagawa, Japan (Ide, T.)
ISBN 0769522785
ISSN 15504786
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2005-11-27
Publisher Place USA
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Size (in Bytes) 144.78 kB


Source: IEEE Xplore Digital Library