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This article introduces the autoencoder graph ensemble model (AEGEM), a novel ensemble-based framework designed to enhance performance in both endmember extraction and abundance estimation. In the ...

Moreover, most of the schemes decouple the model learning process, resulting in suboptimal performance. To tackle these challenges, in this paper, we propose a unified Unsupervised Gaussian Mixture ...

This repository contains numerous applications of autoencoder neural networks. Projects include image denoising, detection of infected cells, and processing of the MNIST dataset. Each application ...