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Title: Final Report: Parallel Tensor Decompositions for Massive Heterogeneous Incomplete Data (LDRD Project 199986).

Abstract

We extended the fundamental capabilities of tensor decomposition to a broader range of problems - handling new data types and larger problems. This has implications for data analysis across a range of applications in sensor monitoring, cybersecurity, treaty verification, signal processing, etc. Identifies latent structure within data, enabling anomaly detection, process monitoring, scientific discovery.

Authors:
Publication Date:
Research Org.:
Sandia National Lab. (SNL-CA), Livermore, CA (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1569136
Report Number(s):
SAND2019-11610
679608
DOE Contract Number:  
AC04-94AL85000
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English

Citation Formats

Kolda, Tamara G. Final Report: Parallel Tensor Decompositions for Massive Heterogeneous Incomplete Data (LDRD Project 199986).. United States: N. p., 2019. Web. doi:10.2172/1569136.
Kolda, Tamara G. Final Report: Parallel Tensor Decompositions for Massive Heterogeneous Incomplete Data (LDRD Project 199986).. United States. doi:10.2172/1569136.
Kolda, Tamara G. Sun . "Final Report: Parallel Tensor Decompositions for Massive Heterogeneous Incomplete Data (LDRD Project 199986).". United States. doi:10.2172/1569136. https://www.osti.gov/servlets/purl/1569136.
@article{osti_1569136,
title = {Final Report: Parallel Tensor Decompositions for Massive Heterogeneous Incomplete Data (LDRD Project 199986).},
author = {Kolda, Tamara G.},
abstractNote = {We extended the fundamental capabilities of tensor decomposition to a broader range of problems - handling new data types and larger problems. This has implications for data analysis across a range of applications in sensor monitoring, cybersecurity, treaty verification, signal processing, etc. Identifies latent structure within data, enabling anomaly detection, process monitoring, scientific discovery.},
doi = {10.2172/1569136},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {9}
}