Low-rank Matrix Approximation Based on Intermingled Randomized Decomposition

Maboud F. Kaloorazi, Jie Chen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations

Abstract

This work introduces a novel matrix decomposition method termed Intermingled Randomized Singular Value Decomposition (InR-SVD), along with an InR-SVD variant powered by the power iteration scheme. InR-SVD computes a low-rank approximation to an input matrix by means of random sampling techniques. Given a large and dense m × n matrix, InR-SVD constructs a low-rank approximation with a few passes over the data in O(mnk) floating-point operations, where k is much smaller than m and n. Furthermore, InR-SVD can exploit modern computational platforms and thereby being optimized for maximum efficiency. InR-SVD is applied to synthetic data as well as real data in image reconstruction and robust principal component analysis problems. Simulations show that InR-SVD outperforms existing approaches.

Original languageEnglish
Title of host publication2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages7475-7479
Number of pages5
ISBN (Electronic)9781479981311
DOIs
StatePublished - May 2019
Event44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Brighton, United Kingdom
Duration: 12 May 201917 May 2019

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2019-May
ISSN (Print)1520-6149

Conference

Conference44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019
Country/TerritoryUnited Kingdom
CityBrighton
Period12/05/1917/05/19

Keywords

  • image reconstruction
  • low-rank approximation
  • Matrix decomposition
  • randomized algorithms
  • robust PCA

Fingerprint

Dive into the research topics of 'Low-rank Matrix Approximation Based on Intermingled Randomized Decomposition'. Together they form a unique fingerprint.

Cite this