# Archetypal Analysis for Binary Data

arXiv: https://arxiv.org/abs/2502.04172 · HTML: https://mlsanity.com/paper/2502.04172/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 17 references that are in the map.
- First posted on arXiv 6 Feb 2025.

## Builds on (most cited first, 4 of 4)

- [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)
- [Finding archetypal patterns for binary questionnaires](https://mlsanity.com/paper/2003.00043/) (2020)
- [Probabilistic methods for approximate archetypal analysis](https://mlsanity.com/paper/2108.05767/) (2021)

## Built on by (most cited first, 2 of 2)

- [Archetypal SAE: Adaptive and Stable Dictionary Learning for Concept Extraction in Large Vision Models](https://mlsanity.com/paper/2502.12892/) (2025)
- [A Survey on Archetypal Analysis](https://mlsanity.com/paper/2504.12392/) (2025)

## Abstract (arXiv)

Archetypal analysis (AA) is a matrix decomposition method that identifies distinct patterns using convex combinations of the data points denoted archetypes with each data point in turn reconstructed as convex combinations of the archetypes. AA thereby forms a polytope representing trade-offs of the distinct aspects in the data. Most existing methods for AA are designed for continuous data and do not exploit the structure of the data distribution. In this paper, we propose two new optimization frameworks for archetypal analysis for binary data. i) A second order approximation of the AA likelihood based on the Bernoulli distribution with efficient closed-form updates using an active set procedure for learning the convex combinations defining the archetypes, and a sequential minimal optimization strategy for learning the observation specific reconstructions. ii) A Bernoulli likelihood based version of the principal convex hull analysis (PCHA) algorithm originally developed for least squares optimization. We compare these approaches with the only existing binary AA procedure relying on multiplicative updates and demonstrate their superiority on both synthetic and real binary data. Notably, the proposed optimization frameworks for AA can easily be extended to other data distributions providing generic efficient optimization frameworks for AA based on tailored likelihood functions reflecting the underlying data distribution.