Search Results for author:
Found 6 papers, 1 papers with code
Process Modeling, Hidden Markov Models, and Non-negative Tensor Factorization with Model Selection
Process monitoring allows users to gauge the involvement of an organization in an industrial process or predict the degradation or aging of machine parts in processes taking place at a remote location.
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.
