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Self organising maps for visualising and modelling
The paper describes the motivation of SOMs (Self Organising Maps) and how they are generally more accessible due to the wider available modern, more powerful, cost-effective computers. Their advantages compared to Principal Components Analysis and Partial Least Squares are discussed. These allow app...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2012
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3395104/ https://www.ncbi.nlm.nih.gov/pubmed/22594434 http://dx.doi.org/10.1186/1752-153X-6-S2-S1 |
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author | Brereton, Richard G |
author_facet | Brereton, Richard G |
author_sort | Brereton, Richard G |
collection | PubMed |
description | The paper describes the motivation of SOMs (Self Organising Maps) and how they are generally more accessible due to the wider available modern, more powerful, cost-effective computers. Their advantages compared to Principal Components Analysis and Partial Least Squares are discussed. These allow application to non-linear data, are not so dependent on least squares solutions, normality of errors and less influenced by outliers. In addition there are a wide variety of intuitive methods for visualisation that allow full use of the map space. Modern problems in analytical chemistry include applications to cultural heritage studies, environmental, metabolomic and biological problems result in complex datasets. Methods for visualising maps are described including best matching units, hit histograms, unified distance matrices and component planes. Supervised SOMs for classification including multifactor data and variable selection are discussed as is their use in Quality Control. The paper is illustrated using four case studies, namely the Near Infrared of food, the thermal analysis of polymers, metabolomic analysis of saliva using NMR, and on-line HPLC for pharmaceutical process monitoring. |
format | Online Article Text |
id | pubmed-3395104 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-33951042012-07-16 Self organising maps for visualising and modelling Brereton, Richard G Chem Cent J Proceedings The paper describes the motivation of SOMs (Self Organising Maps) and how they are generally more accessible due to the wider available modern, more powerful, cost-effective computers. Their advantages compared to Principal Components Analysis and Partial Least Squares are discussed. These allow application to non-linear data, are not so dependent on least squares solutions, normality of errors and less influenced by outliers. In addition there are a wide variety of intuitive methods for visualisation that allow full use of the map space. Modern problems in analytical chemistry include applications to cultural heritage studies, environmental, metabolomic and biological problems result in complex datasets. Methods for visualising maps are described including best matching units, hit histograms, unified distance matrices and component planes. Supervised SOMs for classification including multifactor data and variable selection are discussed as is their use in Quality Control. The paper is illustrated using four case studies, namely the Near Infrared of food, the thermal analysis of polymers, metabolomic analysis of saliva using NMR, and on-line HPLC for pharmaceutical process monitoring. BioMed Central 2012-05-02 /pmc/articles/PMC3395104/ /pubmed/22594434 http://dx.doi.org/10.1186/1752-153X-6-S2-S1 Text en Copyright ©2012 Brereton; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Proceedings Brereton, Richard G Self organising maps for visualising and modelling |
title | Self organising maps for visualising and modelling |
title_full | Self organising maps for visualising and modelling |
title_fullStr | Self organising maps for visualising and modelling |
title_full_unstemmed | Self organising maps for visualising and modelling |
title_short | Self organising maps for visualising and modelling |
title_sort | self organising maps for visualising and modelling |
topic | Proceedings |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3395104/ https://www.ncbi.nlm.nih.gov/pubmed/22594434 http://dx.doi.org/10.1186/1752-153X-6-S2-S1 |
work_keys_str_mv | AT breretonrichardg selforganisingmapsforvisualisingandmodelling |