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Parallel Algorithms for Low-Rank Approximations of Matrices and Tensors

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abstract
Low-rank approximations are useful in the compression and interpretation of large datasets. Distributed parallel algorithms of such approximations, like those for matrices and tensors, are applicable for even larger datasets that cannot conceivably fit on one computer. In this thesis I will present parallelizing two such approximation algorithms: Hierarchical Nonnegative Matrix Factorization, and Tensor Train Rounding. In both cases, the distributed parallel algorithms outperform the state of the art.
subject
linear algebra
low-rank approximation
nonnegative matrix factorization
parallel algorithms
tensor decompositions
tensor train
contributor
Manning, Lawton (author)
Ballard, Grey (committee chair)
Erway, Jennifer (committee member)
Cho, Samuel (committee member)
date
2021-06-03T08:36:13Z (accessioned)
2021-06-03T08:36:13Z (available)
2021 (issued)
degree
Computer Science (discipline)
identifier
http://hdl.handle.net/10339/98822 (uri)
language
en (iso)
publisher
Wake Forest University
title
Parallel Algorithms for Low-Rank Approximations of Matrices and Tensors
type
Thesis

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