Title: Source identification by non-negative matrix factorization combined with semi-supervised clustering

Abstract

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.

Inventors:

Alexandrov, Boian S.; Alexandrov, Ludmil B.; Iliev, Filip L.; Stanev, Valentin G.; Vesselinov, Velimir V.

Issue Date:

2023-09-05

Research Org.:

Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Org.:

USDOE

OSTI Identifier:

2222289

Patent Number(s):

11748657

Application Number:

17/020,504

Assignee:

Triad National Security, LLC (Los Alamos, NM)

Patent Classifications (CPCs):

G - PHYSICS G06 - COMPUTING G06F - ELECTRIC DIGITAL DATA PROCESSING

G - PHYSICS G06 - COMPUTING G06N - COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS

Show more

G - PHYSICS G06 - COMPUTING G06F - ELECTRIC DIGITAL DATA PROCESSING

G06F17/16 - Matrix or vector computation {, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization

G - PHYSICS G06 - COMPUTING G06N - COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS

G06N20/00 - Machine learning

Show less

DOE Contract Number:  

AC52-06NA25396

Resource Type:

Patent

Resource Relation:

Patent File Date: 09/14/2020

Country of Publication:

United States

Language:

English