# Clustered Multitask Nonnegative Matrix Factorization for Spectral Unmixing of Hyperspectral Data

arXiv: https://arxiv.org/abs/1905.08032 · HTML: https://mlsanity.com/paper/1905.08032/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 May 2019.

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

- [Spectral Unmixing of Hyperspectral Images Based on Block Sparse Structure](https://mlsanity.com/paper/2204.04638/) (2022)

## Abstract (arXiv)

In this paper, the new algorithm based on clustered multitask network is proposed to solve spectral unmixing problem in hyperspectral imagery. In the proposed algorithm, the clustered network is employed. Each pixel in the hyperspectral image considered as a node in this network. The nodes in the network are clustered using the fuzzy c-means clustering method. Diffusion least mean square strategy has been used to optimize the proposed cost function. To evaluate the proposed method, experiments are conducted on synthetic and real datasets. Simulation results based on spectral angle distance, abundance angle distance and reconstruction error metrics illustrate the advantage of the proposed algorithm compared with other methods.