eNMF is a new exterior-point algorithm for NMF that initializes from unconstrained factorization, applies a rotation to reach the nonnegative boundary, and empirically outperforms 81 baseline combinations on real and synthetic data.
IEICE transactions on fundamentals of electronics, communications and computer sciences , volume=
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A parametric autoencoder with non-negativity and softmax constraints learns interpretable latent chemical components and couples them to kinetics and heat release for improved reduced-order modeling of decomposition.
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An Exterior Method for Nonnegative Matrix Factorization
eNMF is a new exterior-point algorithm for NMF that initializes from unconstrained factorization, applies a rotation to reach the nonnegative boundary, and empirically outperforms 81 baseline combinations on real and synthetic data.
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A Data-Driven Parametric Reduced-Order Chemical Kinetics Model Derived from Atomistic Simulations
A parametric autoencoder with non-negativity and softmax constraints learns interpretable latent chemical components and couples them to kinetics and heat release for improved reduced-order modeling of decomposition.