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- research-articleJuly 2024
Generating Hidden Markov Models from Process Models Through Nonnegative Tensor Factorization
ACM Transactions on Modeling and Computer Simulation (TOMACS), Volume 34, Issue 4Article No.: 21, Pages 1–19https://doi.org/10.1145/3664813Monitoring of industrial processes is a critical capability in industry and in government to ensure reliability of production cycles, quick emergency response, and national security. Process monitoring allows users to gauge the progress of an organization ...
- correctionSeptember 2023
- research-articleSeptember 2023
- research-articleSeptember 2023
Distributed non-negative RESCAL with automatic model selection for exascale data
- Manish Bhattarai,
- Namita kharat,
- Ismael Boureima,
- Erik Skau,
- Benjamin Nebgen,
- Hristo Djidjev,
- Sanjay Rajopadhye,
- James P. Smith,
- Boian Alexandrov
Journal of Parallel and Distributed Computing (JPDC), Volume 179, Issue CSep 2023https://doi.org/10.1016/j.jpdc.2023.04.010AbstractWith the boom in the development of computer hardware and software, social media, IoT platforms, and communications, there has been exponential growth in the volume of data produced worldwide. Among these data, relational datasets are ...
Graphical abstract Highlights- The first distributed RESCAL implementation to estimate latent features.
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- research-articleMarch 2023
General-purpose Unsupervised Cyber Anomaly Detection via Non-negative Tensor Factorization
- Maksim E. Eren,
- Juston S. Moore,
- Erik Skau,
- Elisabeth Moore,
- Manish Bhattarai,
- Gopinath Chennupati,
- Boian S. Alexandrov
Digital Threats: Research and Practice (DTRAP), Volume 4, Issue 1Article No.: 6, Pages 1–28https://doi.org/10.1145/3519602Distinguishing malicious anomalous activities from unusual but benign activities is a fundamental challenge for cyber defenders. Prior studies have shown that statistical user behavior analysis yields accurate detections by learning behavior profiles from ...
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- research-articleJuly 2022
Factorization of Binary Matrices: Rank Relations, Uniqueness and Model Selection of Boolean Decomposition
ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 16, Issue 6Article No.: 112, Pages 1–24https://doi.org/10.1145/3522594The application of binary matrices are numerous. Representing a matrix as a mixture of a small collection of latent vectors via low-rank decomposition is often seen as an advantageous method to interpret and analyze data. In this work, we examine the ...
- ArticleJune 2021
Topic Analysis of Superconductivity Literature by Semantic Non-negative Matrix Factorization
AbstractWe analyze a corpus consisting of more than 17,000 abstracts in the general field of superconductivity, extracted from the arXiv – an online repository of scientific articles. We utilize a recently developed topic modeling method called SeNMFk, ...
- ArticleJune 2021
Nonnegative Tensor-Train Low-Rank Approximations of the Smoluchowski Coagulation Equation
AbstractWe present a finite difference approximation of the nonnegative solutions of the two dimensional Smoluchowski equation by a nonnegative low-order tensor factorization. Two different implementations are compared. The first one is based on a full ...
- research-articleSeptember 2020
Distributed non-negative matrix factorization with determination of the number of latent features
The Journal of Supercomputing (JSCO), Volume 76, Issue 9Sep 2020, Pages 7458–7488https://doi.org/10.1007/s11227-020-03181-6AbstractThe holistic analysis and understanding of the latent (that is, not directly observable) variables and patterns buried in large datasets is crucial for data-driven science, decision making and emergency response. Such exploratory analyses require ...
- research-articleMarch 2017
Image classification: A hierarchical dictionary learning approach
2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)Mar 2017, Pages 2597–2601https://doi.org/10.1109/ICASSP.2017.7952626Hierarchical dictionary learning seeks multiple dictionaries at different image scales to capture complementary coherent characteristics. We propose a method to learn a hierarchy of two overcomplete synthesis dictionaries with an image classification ...
- research-articleMarch 2016
Pansharpening via coupled triple factorization dictionary learning
2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)Pages 1234–1237https://doi.org/10.1109/ICASSP.2016.7471873Data fusion is the operation of integrating data from different modalities to construct a single consistent representation. This paper proposes variations of coupled dictionary learning through an additional factorization. One variation of this model is ...