An Accelerated Accuracy-enhanced Randomized Singular Value Decomposition for Factorizing Matrices with Low-rank Structure

Joseph Roger Arhin; Francis Sam; Kenneth Coker; Toufic Seini

doi:doi:10.11648/j.pamj.20200904.11

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An Accelerated Accuracy-enhanced Randomized Singular Value Decomposition for Factorizing Matrices with Low-rank Structure

Joseph Roger Arhin, Francis Sam, Kenneth Coker, Toufic Seini

Published in Pure and Applied Mathematics Journal (Volume 9, Issue 4)

Received: 24 June 2020 Accepted: 13 July 2020 Published: 28 July 2020

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Abstract

Big data has in recent years gained ground in many scientific and engineering problems. It seems to some extent prohibitive for traditional matrix decomposition methods (i.e. QR, SVD, EVD, etc.) to handle such large-scale problems involving data matrix. Many researchers have developed several algorithms to decompose such big data matrices. An accuracy-enhanced randomized singular value decomposition method (referred to as AE-RSVDM) with orthonormalization recently becomes the state-of-the-art to factorize large data matrices with satisfactory speed and accuracy. In our paper, low-rank matrix approximations based on randomization are studied, with emphasis on accelerating the computational efficiency on large data matrices. By this, we accelerate the AE-RSVDM with modified normalized power iteration to result in an accelerated version. The accelerated version is grounded on a two-stage scheme. The first stage seeks to find the range of a sketch matrix which involves a Gaussian random matrix. A low-dimensional space is then created from the high-dimensional data matrix via power iteration. Numerical experiments on matrices of different sizes demonstrate that our accelerated variant achieves speedups while attaining the same reconstruction error as the AE-RSVDM with orthonormalization. And with data from Google art project, we have made known the computational speed-up of the accelerated variant over the AE-RSVDM algorithm for decomposing large data matrices with low-rank form.

Published in	Pure and Applied Mathematics Journal (Volume 9, Issue 4)
DOI	10.11648/j.pamj.20200904.11
Page(s)	64-73
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Low-rank, Singular Value Decomposition (SVD), Accelerated AE-RSVDM, Orthonormalization, Power Iteration

References

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[7]	Khoromskij BN (2011) O (dlogN)-quantics approximation of N-d tensors in high-dimensional numerical modeling. Constructive Approximation, 34 (2), 257-280.
[8]	B. N. Erichson, S. L. Brunton and N. J. Kutz, “Compressed singular value decomposition for image and video processing,” In Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 1880-1888, 2017.
[9]	Calvetti D, Reichel L and Sorensen DC (1994) An implicitly restarted Lanczos method for large symmetric eigenvalue problems. Electronic Transactions on Numerical Analysis, 2 (1), 21.
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[11]	T. Sarlos, “Improved approximation algorithms for large matrices via random projections,” In 2006 47th Annual IEEE Symposium on Foundations of Computer Science (FOCS'06), pp. 143-152, 2006.
[12]	Martinsson, P-G, Rokhlin V and Tygert M (2011) A randomized algorithm for the decomposition of matrices. Applied and Computational Harmonic Analysis, 30 (1), 47-68.
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[14]	Martinsson, P-G (2019) Randomized methods for matrix computations. The Mathematics of Data, 25, 187-231.
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[19]	Chen Z and Dongarra JJ (2005) Condition numbers of Gaussian random matrices. SIAM Journal on Matrix Analysis and Applications, 27 (3), 603-620.

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APA Style

Joseph Roger Arhin, Francis Sam, Kenneth Coker, Toufic Seini. (2020). An Accelerated Accuracy-enhanced Randomized Singular Value Decomposition for Factorizing Matrices with Low-rank Structure. Pure and Applied Mathematics Journal, 9(4), 64-73. https://doi.org/10.11648/j.pamj.20200904.11

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ACS Style

Joseph Roger Arhin; Francis Sam; Kenneth Coker; Toufic Seini. An Accelerated Accuracy-enhanced Randomized Singular Value Decomposition for Factorizing Matrices with Low-rank Structure. Pure Appl. Math. J. 2020, 9(4), 64-73. doi: 10.11648/j.pamj.20200904.11

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AMA Style

Joseph Roger Arhin, Francis Sam, Kenneth Coker, Toufic Seini. An Accelerated Accuracy-enhanced Randomized Singular Value Decomposition for Factorizing Matrices with Low-rank Structure. Pure Appl Math J. 2020;9(4):64-73. doi: 10.11648/j.pamj.20200904.11

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@article{10.11648/j.pamj.20200904.11,
  author = {Joseph Roger Arhin and Francis Sam and Kenneth Coker and Toufic Seini},
  title = {An Accelerated Accuracy-enhanced Randomized Singular Value Decomposition for Factorizing Matrices with Low-rank Structure},
  journal = {Pure and Applied Mathematics Journal},
  volume = {9},
  number = {4},
  pages = {64-73},
  doi = {10.11648/j.pamj.20200904.11},
  url = {https://doi.org/10.11648/j.pamj.20200904.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.pamj.20200904.11},
  abstract = {Big data has in recent years gained ground in many scientific and engineering problems. It seems to some extent prohibitive for traditional matrix decomposition methods (i.e. QR, SVD, EVD, etc.) to handle such large-scale problems involving data matrix. Many researchers have developed several algorithms to decompose such big data matrices. An accuracy-enhanced randomized singular value decomposition method (referred to as AE-RSVDM) with orthonormalization recently becomes the state-of-the-art to factorize large data matrices with satisfactory speed and accuracy. In our paper, low-rank matrix approximations based on randomization are studied, with emphasis on accelerating the computational efficiency on large data matrices. By this, we accelerate the AE-RSVDM with modified normalized power iteration to result in an accelerated version. The accelerated version is grounded on a two-stage scheme. The first stage seeks to find the range of a sketch matrix which involves a Gaussian random matrix. A low-dimensional space is then created from the high-dimensional data matrix via power iteration. Numerical experiments on matrices of different sizes demonstrate that our accelerated variant achieves speedups while attaining the same reconstruction error as the AE-RSVDM with orthonormalization. And with data from Google art project, we have made known the computational speed-up of the accelerated variant over the AE-RSVDM algorithm for decomposing large data matrices with low-rank form.},
 year = {2020}
}

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TY - JOUR
T1 - An Accelerated Accuracy-enhanced Randomized Singular Value Decomposition for Factorizing Matrices with Low-rank Structure
AU - Joseph Roger Arhin
AU - Francis Sam
AU - Kenneth Coker
AU - Toufic Seini
Y1 - 2020/07/28
PY - 2020
N1 - https://doi.org/10.11648/j.pamj.20200904.11
DO - 10.11648/j.pamj.20200904.11
T2 - Pure and Applied Mathematics Journal
JF - Pure and Applied Mathematics Journal
JO - Pure and Applied Mathematics Journal
SP - 64
EP - 73
PB - Science Publishing Group
SN - 2326-9812
UR - https://doi.org/10.11648/j.pamj.20200904.11
AB - Big data has in recent years gained ground in many scientific and engineering problems. It seems to some extent prohibitive for traditional matrix decomposition methods (i.e. QR, SVD, EVD, etc.) to handle such large-scale problems involving data matrix. Many researchers have developed several algorithms to decompose such big data matrices. An accuracy-enhanced randomized singular value decomposition method (referred to as AE-RSVDM) with orthonormalization recently becomes the state-of-the-art to factorize large data matrices with satisfactory speed and accuracy. In our paper, low-rank matrix approximations based on randomization are studied, with emphasis on accelerating the computational efficiency on large data matrices. By this, we accelerate the AE-RSVDM with modified normalized power iteration to result in an accelerated version. The accelerated version is grounded on a two-stage scheme. The first stage seeks to find the range of a sketch matrix which involves a Gaussian random matrix. A low-dimensional space is then created from the high-dimensional data matrix via power iteration. Numerical experiments on matrices of different sizes demonstrate that our accelerated variant achieves speedups while attaining the same reconstruction error as the AE-RSVDM with orthonormalization. And with data from Google art project, we have made known the computational speed-up of the accelerated variant over the AE-RSVDM algorithm for decomposing large data matrices with low-rank form.
VL - 9
IS - 4
ER -

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Author Information

Joseph Roger Arhin

School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu, China
Francis Sam

School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu, China
Kenneth Coker

School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China
Toufic Seini

School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu, China

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Plain Text BibTeX RIS

APA Style

Joseph Roger Arhin, Francis Sam, Kenneth Coker, Toufic Seini. (2020). An Accelerated Accuracy-enhanced Randomized Singular Value Decomposition for Factorizing Matrices with Low-rank Structure. Pure and Applied Mathematics Journal, 9(4), 64-73. https://doi.org/10.11648/j.pamj.20200904.11

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ACS Style

Joseph Roger Arhin; Francis Sam; Kenneth Coker; Toufic Seini. An Accelerated Accuracy-enhanced Randomized Singular Value Decomposition for Factorizing Matrices with Low-rank Structure. Pure Appl. Math. J. 2020, 9(4), 64-73. doi: 10.11648/j.pamj.20200904.11

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AMA Style

Joseph Roger Arhin, Francis Sam, Kenneth Coker, Toufic Seini. An Accelerated Accuracy-enhanced Randomized Singular Value Decomposition for Factorizing Matrices with Low-rank Structure. Pure Appl Math J. 2020;9(4):64-73. doi: 10.11648/j.pamj.20200904.11

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@article{10.11648/j.pamj.20200904.11,
  author = {Joseph Roger Arhin and Francis Sam and Kenneth Coker and Toufic Seini},
  title = {An Accelerated Accuracy-enhanced Randomized Singular Value Decomposition for Factorizing Matrices with Low-rank Structure},
  journal = {Pure and Applied Mathematics Journal},
  volume = {9},
  number = {4},
  pages = {64-73},
  doi = {10.11648/j.pamj.20200904.11},
  url = {https://doi.org/10.11648/j.pamj.20200904.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.pamj.20200904.11},
  abstract = {Big data has in recent years gained ground in many scientific and engineering problems. It seems to some extent prohibitive for traditional matrix decomposition methods (i.e. QR, SVD, EVD, etc.) to handle such large-scale problems involving data matrix. Many researchers have developed several algorithms to decompose such big data matrices. An accuracy-enhanced randomized singular value decomposition method (referred to as AE-RSVDM) with orthonormalization recently becomes the state-of-the-art to factorize large data matrices with satisfactory speed and accuracy. In our paper, low-rank matrix approximations based on randomization are studied, with emphasis on accelerating the computational efficiency on large data matrices. By this, we accelerate the AE-RSVDM with modified normalized power iteration to result in an accelerated version. The accelerated version is grounded on a two-stage scheme. The first stage seeks to find the range of a sketch matrix which involves a Gaussian random matrix. A low-dimensional space is then created from the high-dimensional data matrix via power iteration. Numerical experiments on matrices of different sizes demonstrate that our accelerated variant achieves speedups while attaining the same reconstruction error as the AE-RSVDM with orthonormalization. And with data from Google art project, we have made known the computational speed-up of the accelerated variant over the AE-RSVDM algorithm for decomposing large data matrices with low-rank form.},
 year = {2020}
}

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