# SUnAA: Sparse Unmixing using Archetypal Analysis

arXiv: https://arxiv.org/abs/2308.04771 · HTML: https://mlsanity.com/paper/2308.04771/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 20 references that are in the map.
- First posted on arXiv 9 Aug 2023.

## Builds on (most cited first, 5 of 5)

- [Spectral Variability in Hyperspectral Data Unmixing: A Comprehensive Review](https://mlsanity.com/paper/2001.07307/) (2020)
- [Fast and Robust Archetypal Analysis for Representation Learning](https://mlsanity.com/paper/1405.6472/) (2014)
- [Tech Report: A Fast Multiscale Spatial Regularization for Sparse Hyperspectral Unmixing](https://mlsanity.com/paper/1712.01770/) (2017)
- [Entropic Descent Archetypal Analysis for Blind Hyperspectral Unmixing](https://mlsanity.com/paper/2209.11002/) (2022)
- [Superpixel Based Graph Laplacian Regularization for Sparse Hyperspectral Unmixing](https://mlsanity.com/paper/2007.14033/) (2020)

## Built on by (most cited first, 5 of 5)

- [Image Processing and Machine Learning for Hyperspectral Unmixing: An Overview and the HySUPP Python Package](https://mlsanity.com/paper/2308.09375/) (2023)
- [A Survey on Archetypal Analysis](https://mlsanity.com/paper/2504.12392/) (2025)
- [Fast Semisupervised Unmixing Using Nonconvex Optimization](https://mlsanity.com/paper/2401.12609/) (2024)
- [MiSiSUn: Minimum Simplex Semisupervised Unmixing](https://mlsanity.com/paper/2603.20263/) (2026)
- [Polyhedral Unmixing: Bridging Semantic Segmentation with Hyperspectral Unmixing via Polyhedral-Cone Partitioning](https://mlsanity.com/paper/2603.29438/) (2026)

## Abstract (arXiv)

This paper introduces a new sparse unmixing technique using archetypal analysis (SUnAA). First, we design a new model based on archetypal analysis. We assume that the endmembers of interest are a convex combination of endmembers provided by a spectral library and that the number of endmembers of interest is known. Then, we propose a minimization problem. Unlike most conventional sparse unmixing methods, here the minimization problem is non-convex. We minimize the optimization objective iteratively using an active set algorithm. Our method is robust to the initialization and only requires the number of endmembers of interest. SUnAA is evaluated using two simulated datasets for which results confirm its better performance over other conventional and advanced techniques in terms of signal-to-reconstruction error. SUnAA is also applied to Cuprite dataset and the results are compared visually with the available geological map provided for this dataset. The qualitative assessment demonstrates the successful estimation of the minerals abundances and significantly improves the detection of dominant minerals compared to the conventional regression-based sparse unmixing methods. The Python implementation of SUnAA can be found at: https://github.com/BehnoodRasti/SUnAA.