SmartTensors/NMFk.jl
Nonnegative Matrix Factorization + k-means clustering and physics constraints for Unsupervised and Physics-Informed Machine Learning
This tool helps scientists and engineers automatically discover the optimal number of underlying "signals" or features hidden within complex datasets, without prior knowledge. You provide your raw measurements or simulation outputs, and it identifies the distinct components and how they combine to form your observations. This is ideal for researchers in fields like environmental science, materials science, or geoscience.
Use this if you have complex data from experiments or simulations and need to identify the core, unobserved processes or sources that explain your measurements.
Not ideal if you already know the exact number of underlying components you are looking for or if your data does not involve mixed, non-negative signals.
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GPL-3.0
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Last pushed
Mar 20, 2026
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