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...
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| Tipo de recurso: | artículo |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2020 |
| País: | España |
| Institución: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:dnet:digitalcsic_::b355dfc5235d060c26e60ccc22cc7e9f |
| Acceso en línea: | http://hdl.handle.net/10261/228529 |
| Access Level: | acceso abierto |
| Palabra clave: | Multivariate curve resolution MCR‐ALS |
| Sumario: | 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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