# Biarchetype analysis: simultaneous learning of observations and features based on extremes

arXiv: https://arxiv.org/abs/2311.11153 · HTML: https://mlsanity.com/paper/2311.11153/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 83 references that are in the map.
- First posted on arXiv 18 Nov 2023.

## Builds on (most cited first, 8 of 8)

- [Fast and Robust Archetypal Analysis for Representation Learning](https://mlsanity.com/paper/1405.6472/) (2014)
- [Probabilistic Archetypal Analysis](https://mlsanity.com/paper/1312.7604/) (2013)
- [Learning Extremal Representations with Deep Archetypal Analysis](https://mlsanity.com/paper/2002.00815/) (2020)
- [Finding Archetypal Spaces Using Neural Networks](https://mlsanity.com/paper/1901.09078/) (2019)
- [Robust multivariate and functional archetypal analysis with application to financial time series analysis](https://mlsanity.com/paper/1810.00919/) (2018)
- [Deep Archetypal Analysis](https://mlsanity.com/paper/1901.10799/) (2019)
- [Finding archetypal patterns for binary questionnaires](https://mlsanity.com/paper/2003.00043/) (2020)
- [Functional archetype and archetypoid analysis](https://mlsanity.com/paper/1601.06911/) (2016)

## Built on by (most cited first, 3 of 3)

- [A Survey on Archetypal Analysis](https://mlsanity.com/paper/2504.12392/) (2025)
- [Multidimensional scaling of two-mode three-way asymmetric dissimilarities: finding archetypal profiles and clustering](https://mlsanity.com/paper/2511.15813/) (2025)
- [Biarchetype analysis for univariate functional data. An application to macroeconomic financial time series](https://mlsanity.com/paper/2606.15881/) (2026)

## Abstract (arXiv)

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.