7 citations · 17 across the 9 of their papers we have counts for
9 papers
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…
Representing asymmetric relationships by h-plots. Discovering the archetypal patterns of cross-journal citation relationships
Aleix Alcacer, Irene Epifanio
This work approaches the multidimensional scaling problem from a novel angle. We introduce a scalable method based on the h-plot, which inherently accommodates asymmetric proximity…
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…
Incorporating Fairness Constraints into Archetypal Analysis
Aleix Alcacer, Irene Epifanio
Archetypal Analysis (AA) is an unsupervised learning method that represents data as convex combinations of extreme patterns called archetypes. While AA provides interpretable and l…
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…