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Found 6 papers, 1 papers with code
Generating Hidden Markov Models from Process Models Through Nonnegative Tensor Factorization
Monitoring of industrial processes is a critical capability in industry and in government to ensure reliability of production cycles, quick emergency response, and national security.
Distributed Out-of-Memory NMF on CPU/GPU Architectures
In this work, we extend NMFk by adding support for dense and sparse matrix operation on multi-node, multi-GPU systems.
Topic Analysis of Superconductivity Literature by Semantic Non-negative Matrix Factorization
We utilize a recently developed topic modeling method called SeNMFk, extending the standard Non-negative Matrix Factorization (NMF) methods by incorporating the semantic structure of the text, and adding a robust system for determining the number of topics.
Boolean Matrix Factorization via Nonnegative Auxiliary Optimization
Instead of solving the BMF problem directly, this approach solves a nonnegative optimization problem with the constraint over an auxiliary matrix whose Boolean structure is identical to the initial Boolean data.
Distributed Non-Negative Tensor Train Decomposition
Tensor train (TT) is a state-of-the-art tensor network introduced for factorization of high-dimensional tensors.
Determination of Latent Dimensionality in International Trade Flow
Nonnegative RESCAL computes a low dimensional tensor representation by finding the latent space containing multiple modalities.
