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Robust [Formula: see text] Approaches to Computing the Geometric Median and Principal and Independent Components
Robust measures are introduced for methods to determine statistically uncorrelated or also statistically independent components spanning data measured in a way that does not permit direct separation of these underlying components. Because of the nonlinear nature of the proposed methods, iterative me...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4946825/ https://www.ncbi.nlm.nih.gov/pubmed/27471346 http://dx.doi.org/10.1007/s10851-016-0637-9 |
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author | Keeling, Stephen L. Kunisch, Karl |
author_facet | Keeling, Stephen L. Kunisch, Karl |
author_sort | Keeling, Stephen L. |
collection | PubMed |
description | Robust measures are introduced for methods to determine statistically uncorrelated or also statistically independent components spanning data measured in a way that does not permit direct separation of these underlying components. Because of the nonlinear nature of the proposed methods, iterative methods are presented for the optimization of merit functions, and local convergence of these methods is proved. Illustrative examples are presented to demonstrate the benefits of the robust approaches, including an application to the processing of dynamic medical imaging. |
format | Online Article Text |
id | pubmed-4946825 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-49468252016-07-26 Robust [Formula: see text] Approaches to Computing the Geometric Median and Principal and Independent Components Keeling, Stephen L. Kunisch, Karl J Math Imaging Vis Article Robust measures are introduced for methods to determine statistically uncorrelated or also statistically independent components spanning data measured in a way that does not permit direct separation of these underlying components. Because of the nonlinear nature of the proposed methods, iterative methods are presented for the optimization of merit functions, and local convergence of these methods is proved. Illustrative examples are presented to demonstrate the benefits of the robust approaches, including an application to the processing of dynamic medical imaging. Springer US 2016-02-24 2016 /pmc/articles/PMC4946825/ /pubmed/27471346 http://dx.doi.org/10.1007/s10851-016-0637-9 Text en © The Author(s) 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Article Keeling, Stephen L. Kunisch, Karl Robust [Formula: see text] Approaches to Computing the Geometric Median and Principal and Independent Components |
title | Robust [Formula: see text] Approaches to Computing the Geometric Median and Principal and Independent Components |
title_full | Robust [Formula: see text] Approaches to Computing the Geometric Median and Principal and Independent Components |
title_fullStr | Robust [Formula: see text] Approaches to Computing the Geometric Median and Principal and Independent Components |
title_full_unstemmed | Robust [Formula: see text] Approaches to Computing the Geometric Median and Principal and Independent Components |
title_short | Robust [Formula: see text] Approaches to Computing the Geometric Median and Principal and Independent Components |
title_sort | robust [formula: see text] approaches to computing the geometric median and principal and independent components |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4946825/ https://www.ncbi.nlm.nih.gov/pubmed/27471346 http://dx.doi.org/10.1007/s10851-016-0637-9 |
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