Optimal transforms of random vectors: The case of successive optimizations

Pablo Soto-Quiros; Anatoli Torokhti

doi:10.1016/j.sigpro.2016.09.020

Optimal transforms of random vectors: The case of successive optimizations

Pablo Soto-Quiros
, Anatoli Torokhti

Escuela de Matemática

University of South Australia

Research output: Contribution to journal › Article › peer-review

2 Scopus citations

Abstract

We propose and justify new transforms of random vectors which provide, under a certain condition, better associated accuracy than that of the optimal transforms, the generic Karhunen-Loève transform and the transform considered by Brillinger. It is achieved by special structures of the proposed transforms which contain more parameters to optimize compared to the known transforms.

Original language	English
Pages (from-to)	183-196
Number of pages	14
Journal	Signal Processing
Volume	132
DOIs	https://doi.org/10.1016/j.sigpro.2016.09.020
State	Published - 1 Mar 2017

Keywords

Karhunen-Loève transform
Least squares linear estimate
Principal Component Analysis
Rank-reduced matrix approximation
Singular value decomposition

Access to Document

10.1016/j.sigpro.2016.09.020

Cite this

@article{e17b673f50764044a9db988db7e7ce9f,

title = "Optimal transforms of random vectors: The case of successive optimizations",

abstract = "We propose and justify new transforms of random vectors which provide, under a certain condition, better associated accuracy than that of the optimal transforms, the generic Karhunen-Lo{\`e}ve transform and the transform considered by Brillinger. It is achieved by special structures of the proposed transforms which contain more parameters to optimize compared to the known transforms.",

keywords = "Karhunen-Lo{\`e}ve transform, Least squares linear estimate, Principal Component Analysis, Rank-reduced matrix approximation, Singular value decomposition",

author = "Pablo Soto-Quiros and Anatoli Torokhti",

note = "Publisher Copyright: {\textcopyright} 2016 Elsevier B.V.",

year = "2017",

month = mar,

day = "1",

doi = "10.1016/j.sigpro.2016.09.020",

language = "Ingl{\'e}s",

volume = "132",

pages = "183--196",

journal = "Signal Processing",

issn = "0165-1684",

}

TY - JOUR

T1 - Optimal transforms of random vectors

T2 - The case of successive optimizations

AU - Soto-Quiros, Pablo

AU - Torokhti, Anatoli

PY - 2017/3/1

Y1 - 2017/3/1

N2 - We propose and justify new transforms of random vectors which provide, under a certain condition, better associated accuracy than that of the optimal transforms, the generic Karhunen-Loève transform and the transform considered by Brillinger. It is achieved by special structures of the proposed transforms which contain more parameters to optimize compared to the known transforms.

AB - We propose and justify new transforms of random vectors which provide, under a certain condition, better associated accuracy than that of the optimal transforms, the generic Karhunen-Loève transform and the transform considered by Brillinger. It is achieved by special structures of the proposed transforms which contain more parameters to optimize compared to the known transforms.

KW - Karhunen-Loève transform

KW - Least squares linear estimate

KW - Principal Component Analysis

KW - Rank-reduced matrix approximation

KW - Singular value decomposition

UR - https://www.scopus.com/pages/publications/84994259667

U2 - 10.1016/j.sigpro.2016.09.020

DO - 10.1016/j.sigpro.2016.09.020

M3 - Artículo

AN - SCOPUS:84994259667

SN - 0165-1684

VL - 132

SP - 183

EP - 196

JO - Signal Processing

JF - Signal Processing

ER -

Optimal transforms of random vectors: The case of successive optimizations

Abstract

Keywords

Access to Document

Other files and links

Fingerprint

Cite this