Biarchetype Analysis: Simultaneous Learning of Observations and Features Based on Extremes
comunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7037
comunitat-uji-handle3:10234/8635
comunitat-uji-handle4:
INVESTIGACIONMetadata
Title
Biarchetype Analysis: Simultaneous Learning of Observations and Features Based on ExtremesDate
2024-05-13Publisher
IEEE Computer SocietyISSN
0162-8828Bibliographic citation
A. Alcacer, I. Epifanio and X. Gual-Arnau, "Biarchetype Analysis: Simultaneous Learning of Observations and Features Based on Extremes," in IEEE Transactions on Pattern Analysis and Machine Intelligence, doi: 10.1109/TPAMI.2024.3400730.Type
info:eu-repo/semantics/articlePublisher version
https://ieeexplore.ieee.org/abstract/document/10530052Version
info:eu-repo/semantics/publishedVersionSubject
Abstract
We introduce a novel exploratory technique, termed biarchetype analysis, which extends archetype analysis to simultaneously identify archetypes of both observations and features. This innovative unsupervised machine ... [+]
We introduce a novel exploratory technique, termed biarchetype analysis, which extends archetype analysis to simultaneously identify archetypes of both observations and features. This innovative unsupervised machine learning tool aims to represent observations and features through instances of pure types, or biarchetypes, which are easily interpretable as they embody mixtures of observations and features. Furthermore, the observations and features are expressed as mixtures of the biarchetypes, which makes the structure of the data easier to understand. We propose an algorithm to solve biarchetype analysis. Although clustering is not the primary aim of this technique, biarchetype analysis is demonstrated to offer significant advantages over biclustering methods, particularly in terms of interpretability. This is attributed to biarchetypes being extreme instances, in contrast to the centroids produced by biclustering, which inherently enhances human comprehension. The application of biarchetype analysis across various machine learning challenges underscores its value, and both the source code and examples are readily accessible in R and Python at https://github.com/aleixalcacer/JA-BIAA . [-]
Is part of
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024.Funder Name
Ministerio de Ciencia e Innovación | Universitat Jaume I
Project code
PID2022-141699NB-I00, PID2020-118763GA-I00, PID2020-115930GA-I00 | UJI-B2020-22, TRANSUJI/2023/6
Rights
© The Authors
info:eu-repo/semantics/openAccess
info:eu-repo/semantics/openAccess
This item appears in the folowing collection(s)
- MAT_Articles [761]