Generalized Karhunen-Loeve transform

Author

Hua, Yingbo ; Liu, Wanquan

Author_Institution

Dept. of Electr. & Electron. Eng., Melbourne Univ., Parkville, Vic., Australia

Volume

5

Issue

6

fYear

1998

fDate

6/1/1998 12:00:00 AM

Firstpage

141

Lastpage

142

Abstract

We present a novel generic tool for data compression and filtering: the generalized Karhunen-Loeve (GKL) transform. The GKL transform minimizes a distance between any given reference and a transformation of some given data where the transform has a predetermined maximum possible rank. The GKL transform is also a generalization of the relative Karhunen-Loeve (RKL) transform by Yamashita and Ogawa (see IEEE Trans. Signal Processing, vol.44, p.661-72, Mar. 1996) where the latter assumes that the given data consist of the given reference (signal) and an independent noise. This letter provides a very simple and yet complete description of the GKL transform and shows useful engineering insights into the GKL transform.

Keywords

Wiener filters; data compression; filtering theory; matrix algebra; singular value decomposition; transforms; GKL transform; SVD; Wiener filter; data compression; filtering; generalized Karhunen-Loeve transform; generic tool; predetermined maximum possible rank; rank reduction; relative Karhunen-Loeve transform; signal processing; Australia Council; Cost function; Covariance matrix; Data compression; Filtering; Information processing; Karhunen-Loeve transforms; Signal processing; Wiener filter;

fLanguage

English

Journal_Title

Signal Processing Letters, IEEE

Publisher

ieee

ISSN

1070-9908

Type

jour

DOI

10.1109/97.681430

Filename

681430