Bayesian model order selection for the Karhunen-Loeve transform and the singular value decomposition

Author

Rajan, J.J. ; Rayner, P.J.W.

Author_Institution

Dept. of Eng., Cambridge Univ., UK

Volume

iv

fYear

1994

fDate

19-22 Apr 1994

Abstract

Discusses model order selection in relation to the discrete Karhunen-Loeve transform (DKLT) and the singular value decomposition (SVD). There are many applications of the DKLT and SVD where it is necessary to discard some of the small singular values that may represent corrupted signal information. Bayesian methods allow to determine the DKLT model order evidence which indicates the optimal number of basis vectors to choose for reconstruction such that the signal is not over-parameterised. Evidence methods can also be used for the SVD to determine the number of singular values (and hence the effective rank) of a singular or ill-conditioned matrix,

Keywords

Bayes methods; matrix algebra; signal reconstruction; singular value decomposition; transforms; Bayesian model order selection; basis vectors; corrupted signal information; discrete Karhunen-Loeve transform; ill-conditioned matrix; reconstruction; singular matrix; singular value decomposition; Bayesian methods; Contracts; Density functional theory; Discrete transforms; Eigenvalues and eigenfunctions; Humans; Karhunen-Loeve transforms; Probability density function; Singular value decomposition; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1994. ICASSP-94., 1994 IEEE International Conference on

Conference_Location

Adelaide, SA

ISSN

1520-6149

Print_ISBN

0-7803-1775-0

Type

conf

DOI

10.1109/ICASSP.1994.389798

Filename

389798