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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)
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author
organization
publishing date
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
2020-01-12 16:30:48
@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},
  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/ws/files/4050062/3993818.pdf},
  doi          = {10.1016/j.sigpro.2012.12.003},
  volume       = {93},
  year         = {2013},
}