Unsupervised Machine Learning Based on Non-negative Tensor Factorization for Analysis of Field Data and Simulation Outputs
V.V. Vesselinov, M. Mudunuru, S. Karra, D. O'Malley, B. Alexandrov
Computational Methods in Water Resources (CMWR), Saint-Malo, France2018DOI: 10.13140/RG.2.2.27777.92005
Summary
Demonstration of unsupervised machine learning based on non-negative tensor factorization for analysis of field data and simulation outputs. The presentation covers the application of these methods to extract hidden features and patterns from complex datasets, with examples from geothermal, geochemical, and climate applications.