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

TL;DR

Biarchetype analysis is a new unsupervised learning method that simultaneously identifies interpretable archetypes of observations and features, enhancing data understanding through extreme, mixture-based representations.

Contribution

This paper introduces biarchetype analysis, extending archetype analysis to jointly learn archetypes of observations and features with an algorithm that improves interpretability over biclustering.

Findings

Biarchetype analysis provides more interpretable data representations.

The method outperforms biclustering in interpretability.

Source code and examples are available in R and Python.

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 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.