# Robust Collaborative Nonnegative Matrix Factorization For Hyperspectral Unmixing (R-CoNMF)

arXiv: https://arxiv.org/abs/1506.04870 · HTML: https://mlsanity.com/paper/1506.04870/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jun 2015.

## Built on by (most cited first, 11 of 11)

- [Deep Hyperspectral Unmixing using Transformer Network](https://mlsanity.com/paper/2203.17076/) (2022)
- [Hyperspectral Unmixing Based on Nonnegative Matrix Factorization: A Comprehensive Review](https://mlsanity.com/paper/2205.09933/) (2022)
- [Image Processing and Machine Learning for Hyperspectral Unmixing: An Overview and the HySUPP Python Package](https://mlsanity.com/paper/2308.09375/) (2023)
- [Using Low-rank Representation of Abundance Maps and Nonnegative Tensor Factorization for Hyperspectral Nonlinear Unmixing](https://mlsanity.com/paper/2103.16204/) (2021)
- [Pixel-to-Abundance Translation: Conditional Generative Adversarial Networks Based on Patch Transformer for Hyperspectral Unmixing](https://mlsanity.com/paper/2312.13127/) (2023)
- [Adaptive Divergence-based Non-negative Latent Factor Analysis](https://mlsanity.com/paper/2203.16214/) (2022)
- [GAUSS: Guided Encoder-Decoder Architecture for Hyperspectral Unmixing with Spatial Smoothness](https://mlsanity.com/paper/2204.07713/) (2022)
- [Spatial-temporal Graph Convolutional Networks with Diversified Transformation for Dynamic Graph Representation Learning](https://mlsanity.com/paper/2408.02704/) (2024)
- [A Novel Spatiotemporal Coupling Graph Convolutional Network](https://mlsanity.com/paper/2408.07087/) (2024)
- [SMILE: A Super-resolution Guided Multi-task Learning Method for Hyperspectral Unmixing](https://mlsanity.com/paper/2509.11093/) (2025)
- [Agentic Multimodal Models for Environmental Hyperspectral Unmixing](https://mlsanity.com/paper/2609.01289/) (2026)

## Abstract (arXiv)

The recently introduced collaborative nonnegative matrix factorization (CoNMF) algorithm was conceived to simultaneously estimate the number of endmembers, the mixing matrix, and the fractional abundances from hyperspectral linear mixtures. This paper introduces R-CoNMF, which is a robust version of CoNMF. The robustness has been added by a) including a volume regularizer which penalizes the distance to a mixing matrix inferred by a pure pixel algorithm; and by b) introducing a new proximal alternating optimization (PAO) algorithm for which convergence to a critical point is guaranteed. Our experimental results indicate that R-CoNMF provides effective estimates both when the number of endmembers are unknown and when they are known.