Análisis Sparse de tensores multidimensionales

González‐García, Nerea

doi:10.14201/gredos.142894

Cited by 3 publications

(4 citation statements)

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“…Very few studies have evidenced the use of sparse penalties in three-way data. Recently, an extension of the three-way Tucker models has been formulated and the C enet Tucker models have been proposed to produce sparse component arrays [14]. Therefore, our contribution opens up the doors for the research and development of new applications sparse in other techniques of the STATIS family or the multivariate analysis of three-way data.…”

Section: Conclusion and Discussionmentioning

confidence: 98%

“…Mathematics 2021, 9, 2094 2 of 15 However, to analyze complex and high-dimensional data [14], it is pertinent to frame the multivariate analysis of data from multiple sets, in the context of a modern classification that allows dealing with large volumes of data. Within the STATIS family of methods, no reference has been found that suggests a solution to the analysis of complex data [15].…”

Section: Introductionmentioning

confidence: 99%

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Sparse STATIS-Dual via Elastic Net

et al. 2021

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Multi-set multivariate data analysis methods provide a way to analyze a series of tables together. In particular, the STATIS-dual method is applied in data tables where individuals can vary from one table to another, but the variables that are analyzed remain fixed. However, when you have a large number of variables or indicators, interpretation through traditional multiple-set methods is complex. For this reason, in this paper, a new methodology is proposed, which we have called Sparse STATIS-dual. This implements the elastic net penalty technique which seeks to retain the most important variables of the model and obtain more precise and interpretable results. As a complement to the new methodology and to materialize its application to data tables with fixed variables, a package is created in the R programming language, under the name Sparse STATIS-dual. Finally, an application to real data is presented and a comparison of results is made between the STATIS-dual and the Sparse STATIS-dual. The proposed method improves the informative capacity of the data and offers more easily interpretable solutions.

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Section: Conclusion and Discussionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Sparse STATIS-Dual via Elastic Net

et al. 2021

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“…The plants of the Erythroxylum genus have many traditional uses, including stimulant and aphrodisiac [2,3]. Phytochemical studies of Erythroxylum species resulted in the isolation of a great variety of compounds such as terpenoids, flavonoids, and tropane alkaloids [4][5][6], the latter being the main focus of interest, accounting for about 50% of the isolated compounds from this genus [7]. Erythroxylum subsessile (Mart.)…”

Section: Introductionmentioning

confidence: 99%

A new tropane alkaloid from the leaves of Erythroxylum subsessile isolated by pH‐zone‐refining counter‐current chromatography

Cruz

Almeida

Fernandes

et al. 2016

J of Separation Science

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Tropane alkaloids are bioactive metabolites with great importance in the pharmaceutical industry and the most important class of natural products found in the Erythroxylum genus. However, these compounds are usually separated by traditional chromatographic techniques, in which the sample is progressively purified in multiple chromatographic steps, resulting in a time- and solvent-consuming procedure. In this work we present the isolation of a novel alkaloid, 6β,7β-dibenzoyloxytropan-3α-ol, together with the two known 3α-benzoyloxynortropan-6β-ol and 3α,6β-dibenzoyloxytropane alkaloids, directly from the crude alkaloid fraction from the leaves of Erythroxylum subsessile, by using a single run pH-zone-refining counter-current chromatography method. The ethyl acetate/water (1:1, v/v) biphasic solvent system with triethylamine and HCl as retention and eluter agents, respectively, was used to isolate tropane alkaloids for the first time. The structures of the isolated alkaloids were elucidated by spectroscopic methods.

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“…El más reciente aporte, en este contexto, de la escuela salmantina, es el método denominado CenetTucker (González-García, 2019), cuyo objetivo fundamental es el de integrar la penalización Elastic Net (Zou & Hastie, 2005), sobre los vectores de cargas de las matrices de cada modo, de tal manera que se obtienen coeficientes exactamente nulos. Como señala su autora, estes es el primer método de descomposición para datos tensoriales que hace la restricción Elastic Net.…”

Section: Escuela Salmantinaunclassified

Contribuciones a los métodos STASIS basados en técnicas de aprendizaje no supervisado

Martínez¹,

Cecilia²

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Primero y ante todo a Dios, por darme fuerzas para seguir adelante y por estar siempre a mi lado en cada paso que doy.A la Dra. Purificación Galindo-Villardón por su confianza y guía para culminar este proyecto, que sin ella no hubiese sido posible. Gracias Puri.A la Dra. Purificación Vicente-Galindo, por aceptar ser mi tutora en este proyecto tan importante para mí.A la Dra. Mitzi Isabel Cubilla Montilla, gracias por tu gran apoyo amiga, por estar siempre y por tus consejos para la culminación de este trabajo, has sido parte fundamental para el logro de este objetivo. Eres extraordinaria.A las doctoras Ana Belén Nieto Librero y Nerea González por sus atinadas guías que fueron fundamentales para terminar este proyecto. Ambas son maravillosas.A todos mis compañer@s y ahora amig@s que me acompañaron durante estos años y siempre me dieron su voz de aliento: Cinthia, Greibin, Joel, Nathalia, Nerea, María, Mariela, Victoria y Zaira. Especialmente quiero agradecerles a Zaira y Victoria por estar conmigo durante aquellas fiestas navideñas que pasamos lejos de nuestras familias, las quiero mucho.A mis colegas y amigos de Panamá por sus continuas palabras de apoyo: Adela,

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Análisis Sparse de tensores multidimensionales

Cited by 3 publications

References 0 publications

Sparse STATIS-Dual via Elastic Net

Sparse STATIS-Dual via Elastic Net

A new tropane alkaloid from the leaves of Erythroxylum subsessile isolated by pH‐zone‐refining counter‐current chromatography

Contribuciones a los métodos STASIS basados en técnicas de aprendizaje no supervisado

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