https://github.com/jonperk318/machine-learning-analysis-of-hyperspectral-data

Using Non-negative Matrix Factorization (NMF) and Variational Autoencoder (VAE) machine learning architectures to analyze spatial and spectral features of hyperspectral cathodoluminescence (CL) spectroscopy images taken from hybrid inorganic-organic perovskite material
https://github.com/jonperk318/machine-learning-analysis-of-hyperspectral-data

data-analysis data-science deep-neural-networks explained-variance hybrid-perovskite hyperspectral-image-classification machine-learning matplotlib nmf non-negative-matrix-factorization python pytorch scikit-learn semi-supervised-learning signal-processing solar-energy spectroscopy unsupervised-learning vae variational-autoencoder

Last synced: about 2 months ago
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Using Non-negative Matrix Factorization (NMF) and Variational Autoencoder (VAE) machine learning architectures to analyze spatial and spectral features of hyperspectral cathodoluminescence (CL) spectroscopy images taken from hybrid inorganic-organic perovskite material

• Host: GitHub
• URL: https://github.com/jonperk318/machine-learning-analysis-of-hyperspectral-data
• Owner: jonperk318
• License: mit
• Created: 2024-01-31T20:32:31.000Z (11 months ago)
• Default Branch: main
• Last Pushed: 2024-09-15T22:18:42.000Z (4 months ago)
• Last Synced: 2024-09-16T00:11:07.051Z (4 months ago)
• Topics: data-analysis, data-science, deep-neural-networks, explained-variance, hybrid-perovskite, hyperspectral-image-classification, machine-learning, matplotlib, nmf, non-negative-matrix-factorization, python, pytorch, scikit-learn, semi-supervised-learning, signal-processing, solar-energy, spectroscopy, unsupervised-learning, vae, variational-autoencoder
• Language: Jupyter Notebook
• Homepage: https://research.jayandsparrow.com/
• Size: 853 MB
• Stars: 2
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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