Computationally Efficient Sparsity-Inducing Coherence Spectrum Estimation of Complete and Non-Complete Data Sets
Angelopoulos, Kostas ; Glentis, George-Othan and Jakobsson, Andreas LU
(2013)
In Signal Processing
93(5).
p.1221-1234
- Abstract
- The magnitude squared coherence (MSC) spectrum is an often used frequency-dependent measure for the linear dependency between two stationary processes, and the recent literature contain several contributions on how to form high-resolution data-dependent and adaptive MSC estimators, and on the efficient implementation of such estimators. In this work, we further this development with the presentation of computationally efficient implementations of the recent iterative adaptive approach (IAA) estimator, present a novel sparse learning via iterative minimization (SLIM) algorithm, discuss extensions to two-dimensional data sets, examining both the case of complete data sets and when some of the observations are missing. The algorithms further... (More)
- The magnitude squared coherence (MSC) spectrum is an often used frequency-dependent measure for the linear dependency between two stationary processes, and the recent literature contain several contributions on how to form high-resolution data-dependent and adaptive MSC estimators, and on the efficient implementation of such estimators. In this work, we further this development with the presentation of computationally efficient implementations of the recent iterative adaptive approach (IAA) estimator, present a novel sparse learning via iterative minimization (SLIM) algorithm, discuss extensions to two-dimensional data sets, examining both the case of complete data sets and when some of the observations are missing. The algorithms further the recent development of exploiting the estimators' inherently low displacement rank of the necessary products of Toeplitz-like matrices, extending these formulations to the coherence estimation using IAA and SLIM formulations. The performance of the proposed algorithms and implementations are illustrated both with theoretical complexity measures and with numerical simulations. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/3193684
- author
- Angelopoulos, Kostas
; Glentis, George-Othan
and Jakobsson, Andreas
LU
- organization
- publishing date
- 2013
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Coherence spectrum, Data adaptive estimators, Efficient algorithms, Sparse estimators
- in
- Signal Processing
- volume
- 93
- issue
- 5
- pages
- 1221 - 1234
- publisher
- Elsevier
- external identifiers
-
- wos:000316586300019
- scopus:84872573312
- ISSN
- 0165-1684
- DOI
- 10.1016/j.sigpro.2012.12.003
- language
- English
- LU publication?
- yes
- id
- 4d9389f7-14df-4671-96f7-79902e3e6b9f (old id 3193684)
- date added to LUP
- 2016-04-01 14:35:02
- date last changed
- 2022-03-29 21:41:15
@article{4d9389f7-14df-4671-96f7-79902e3e6b9f,
abstract = {{The magnitude squared coherence (MSC) spectrum is an often used frequency-dependent measure for the linear dependency between two stationary processes, and the recent literature contain several contributions on how to form high-resolution data-dependent and adaptive MSC estimators, and on the efficient implementation of such estimators. In this work, we further this development with the presentation of computationally efficient implementations of the recent iterative adaptive approach (IAA) estimator, present a novel sparse learning via iterative minimization (SLIM) algorithm, discuss extensions to two-dimensional data sets, examining both the case of complete data sets and when some of the observations are missing. The algorithms further the recent development of exploiting the estimators' inherently low displacement rank of the necessary products of Toeplitz-like matrices, extending these formulations to the coherence estimation using IAA and SLIM formulations. The performance of the proposed algorithms and implementations are illustrated both with theoretical complexity measures and with numerical simulations.}},
author = {{Angelopoulos, Kostas and Glentis, George-Othan and Jakobsson, Andreas}},
issn = {{0165-1684}},
keywords = {{Coherence spectrum; Data adaptive estimators; Efficient algorithms; Sparse estimators}},
language = {{eng}},
number = {{5}},
pages = {{1221--1234}},
publisher = {{Elsevier}},
series = {{Signal Processing}},
title = {{Computationally Efficient Sparsity-Inducing Coherence Spectrum Estimation of Complete and Non-Complete Data Sets}},
url = {{https://lup.lub.lu.se/search/files/4050062/3993818.pdf}},
doi = {{10.1016/j.sigpro.2012.12.003}},
volume = {{93}},
year = {{2013}},
}