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SOURCE IDENTIFICATION BY NON-NEGATIVE MATRIX FACTORIZATION COMBINED WITH SEMI-SUPERVISED CLUSTERING

United States Patent Application

20180060758
A1
View the Complete Application at the US Patent & Trademark Office
Los Alamos National Laboratory - Visit the Technology Transfer Division Website
Machine-learning methods and apparatus are provided to solve blind source separation problems with an unknown number of sources and having a signal propagation model with features such as wave-like propagation, medium-dependent velocity, attenuation, diffusion, and/or advection, between sources and sensors. In exemplary embodiments, multiple trials of non-negative matrix factorization are performed for a fixed number of sources, with selection criteria applied to determine successful trials. A semi-supervised clustering procedure is applied to trial results, and the clustering results are evaluated for robustness using measures for reconstruction quality and cluster separation. The number of sources is determined by comparing these measures for different trial numbers of sources. Source locations and parameters of the signal propagation model can also be determined. Disclosed methods are applicable to a wide range of spatial problems including chemical dispersal, pressure transients, and electromagnetic signals, and also to non-spatial problems such as cancer mutation.
Alexandrov, Boian S. (Santa Fe, NM), Alexandrov, Ludmil B. (Santa Fe, NM), Iliev, Filip L. (Oakland, CA), Stanev, Valentin G. (Crofton, MD), Vesselinov, Velimir V. (Santa Fe, NM)
Los Alamos National Security, LLC (Los Alamos NM)
15/ 690,176
August 29, 2017
ACKNOWLEDGMENT OF GOVERNMENT SUPPORT [0002] This invention was made with government support under Contract No. DE-AC52-06NA25396 awarded by the U.S. Department of Energy. The government has certain rights in the invention.