# Unmixing highly mixed grain size distribution data via maximum volume constrained end member analysis

arXiv: https://arxiv.org/abs/2601.00154 · HTML: https://mlsanity.com/paper/2601.00154/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jan 2026.

## Built on by (most cited first, 1 of 1)

- [Maximum-distance nonnegative matrix factorization for unmixing highly mixed grain-size distribution data: A generalization of AnalySize](https://mlsanity.com/paper/2608.22681/) (2026)

## Abstract (arXiv)

End member analysis (EMA) unmixes grain size distribution (GSD) data into a mixture of end members (EMs), thus helping understand sediment provenance and depositional regimes and processes. In highly mixed data sets, however, many EMA algorithms find EMs which are still a mixture of true EMs. To overcome this, we propose maximum volume constrained EMA (MVC-EMA), which finds EMs as different as possible. We provide a uniqueness theorem and a quadratic programming algorithm for MVC-EMA. Experimental results show that MVC-EMA can effectively find true EMs in highly mixed data sets.