7 citations · 17 across the 9 of their papers we have counts for
7 papers · 1 filter
Biarchetype analysis for univariate functional data. An application to macroeconomic financial time series
Aleix Alcacer, Rafael Benitez, Vicente J. Bolos +1
We introduce biarchetype analysis for the first time in the context of univariate functional data. This unsupervised methodology extends archetype analysis by simultaneously identi…
Archetypal analysis of European 10-year government bond yields with multidimensional scaling of two-mode three-way asymmetric dissimilarities
Aleix Alcacer, Rafael Benitez, Vicente J. Bolos +1
A recent methodology for extracting archetypal profiles from three-way asymmetric proximity data is applied to a dataset comprising 23 x 23 x 3 two-mode, three-way asymmetric dissi…
Archetypal cases for questionnaires with nominal multiple choice questions
Aleix Alcacer, Irene Epifanio
Archetypal analysis serves as an exploratory tool that interprets a collection of observations as convex combinations of pure (extreme) patterns. When these patterns correspond to…
Multidimensional scaling of two-mode three-way asymmetric dissimilarities: finding archetypal profiles and clustering
Mireia Mollar-Gumbau, Aleix Alcacer, Rafael Benitez +2
Multidimensional scaling visualizes dissimilarities among objects and reduces data dimensionality. While many methods address symmetric proximity data, asymmetric and especially th…
A Survey on Archetypal Analysis
Aleix Alcacer, Irene Epifanio, Sebastian Mair +1
Archetypal analysis (AA) was originally proposed in 1994 by Adele Cutler and Leo Breiman as a computational procedure for extracting distinct aspects, so-called archetypes, from ob…
Biarchetype analysis: simultaneous learning of observations and features based on extremes
Aleix Alcacer, Irene Epifanio, Ximo Gual-Arnau
We introduce a novel exploratory technique, termed biarchetype analysis, which extends archetype analysis to simultaneously identify archetypes of both observations and features. T…
Ordinal classification for interval-valued data and interval-valued functional data
Aleix Alcacer, Marina Martínez-Garcia, Irene Epifanio
The aim of ordinal classification is to predict the ordered labels of the output from a set of observed inputs. Interval-valued data refers to data in the form of intervals. For th…