Multivariate curve resolution of multiway data using the multilinearity constraint
The extension of Multivariate Curve Resolution‐Alternating Least Squares (MCR‐ALS) to the analysis of multiway data using the multilinearity constraint is described in detail as one step forward of previous implementations of the trilinearity and quadrilinearity constraints for the analysis of three‐ and four‐way data sets, respectively. As in previous cases, the implementation of the multilinear model for multiway data sets is done algorithmically, within the frame of the alternating least squares (ALS) optimization in the MCR‐ALS method. This implementation is tested using multiway data sets of different complexity, and the obtained results have confirmed the adequacy of the proposed approach. Special advantages of the proposed methodology are that it allows for the implementation of the constraint separately for the different components in their different modes and that it also allows for the introduction of different levels of complexity of the multilinear model, including mixed multilinear models. These two features are especially relevant because they are not present in most of the most used multiway data analysis methods at present.
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Language: | English |
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Wiley-Blackwell
2020-07-12
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Subjects: | Multivariate curve resolution, MCR‐ALS, |
Online Access: | http://hdl.handle.net/10261/228529 |
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dig-idaea-es-10261-2285292021-07-12T04:33:30Z Multivariate curve resolution of multiway data using the multilinearity constraint Tauler, Romà Ministerio de Ciencia, Innovación y Universidades (España) Tauler, Romà [0000-0001-8559-9670] Multivariate curve resolution MCR‐ALS The extension of Multivariate Curve Resolution‐Alternating Least Squares (MCR‐ALS) to the analysis of multiway data using the multilinearity constraint is described in detail as one step forward of previous implementations of the trilinearity and quadrilinearity constraints for the analysis of three‐ and four‐way data sets, respectively. As in previous cases, the implementation of the multilinear model for multiway data sets is done algorithmically, within the frame of the alternating least squares (ALS) optimization in the MCR‐ALS method. This implementation is tested using multiway data sets of different complexity, and the obtained results have confirmed the adequacy of the proposed approach. Special advantages of the proposed methodology are that it allows for the implementation of the constraint separately for the different components in their different modes and that it also allows for the introduction of different levels of complexity of the multilinear model, including mixed multilinear models. These two features are especially relevant because they are not present in most of the most used multiway data analysis methods at present. Ministerio de Ciencia, Universidades y Innovacion (project PID2019‐105732GB‐C21) and Generalitat de Catalunya (project 2017‐SGR‐753) are acknowledged. Peer reviewed 2021-02-04T09:14:50Z 2021-02-04T09:14:50Z 2020-07-12 artículo http://purl.org/coar/resource_type/c_6501 Journal of Chemometrics e3279 (2020) http://hdl.handle.net/10261/228529 10.1002/cem.3279 en #PLACEHOLDER_PARENT_METADATA_VALUE# info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019‐105732GB‐C21 Postprint https://doi.org/10.1002/cem.3279 Sí open Wiley-Blackwell |
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Multivariate curve resolution MCR‐ALS Multivariate curve resolution MCR‐ALS Tauler, Romà Multivariate curve resolution of multiway data using the multilinearity constraint |
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The extension of Multivariate Curve Resolution‐Alternating Least Squares (MCR‐ALS) to the analysis of multiway data using the multilinearity constraint is described in detail as one step forward of previous implementations of the trilinearity and quadrilinearity constraints for the analysis of three‐ and four‐way data sets, respectively. As in previous cases, the implementation of the multilinear model for multiway data sets is done algorithmically, within the frame of the alternating least squares (ALS) optimization in the MCR‐ALS method. This implementation is tested using multiway data sets of different complexity, and the obtained results have confirmed the adequacy of the proposed approach. Special advantages of the proposed methodology are that it allows for the implementation of the constraint separately for the different components in their different modes and that it also allows for the introduction of different levels of complexity of the multilinear model, including mixed multilinear models. These two features are especially relevant because they are not present in most of the most used multiway data analysis methods at present. |
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Ministerio de Ciencia, Innovación y Universidades (España) |
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Ministerio de Ciencia, Innovación y Universidades (España) Tauler, Romà |
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artículo |
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Multivariate curve resolution MCR‐ALS |
author |
Tauler, Romà |
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Tauler, Romà |
title |
Multivariate curve resolution of multiway data using the multilinearity constraint |
title_short |
Multivariate curve resolution of multiway data using the multilinearity constraint |
title_full |
Multivariate curve resolution of multiway data using the multilinearity constraint |
title_fullStr |
Multivariate curve resolution of multiway data using the multilinearity constraint |
title_full_unstemmed |
Multivariate curve resolution of multiway data using the multilinearity constraint |
title_sort |
multivariate curve resolution of multiway data using the multilinearity constraint |
publisher |
Wiley-Blackwell |
publishDate |
2020-07-12 |
url |
http://hdl.handle.net/10261/228529 |
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AT taulerroma multivariatecurveresolutionofmultiwaydatausingthemultilinearityconstraint |
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1777669491876954112 |