Efficient Low-Rank Approximation of Matrices Based on Randomized Pivoted Decomposition

Given a matrix <inline-formula><tex-math notation="LaTeX">\bf A</tex-math></inline-formula> with numerical rank <inline-formula><tex-math notation="LaTeX">k</tex-math></inline-formula>, the two-sided orthogonal decomposition (TSOD)...

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Bibliographic Details
Published in:	IEEE transactions on signal processing Vol. 68; pp. 3575 - 3589
Main Authors:	Kaloorazi, Maboud F., Chen, Jie
Format:	Journal Article
Language:	English
Published:	New York IEEE 2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Acoustics Algorithms Approximation Approximation algorithms Decomposition dimension reduction image recovery low-rank approximation Machine learning algorithms Mathematical analysis Matrix decomposition Randomization randomized numerical linear algebra rank-revealing factorization Signal processing algorithms Sparse matrices Subspaces Surges Upper bounds
ISSN:	1053-587X, 1941-0476
Online Access:	Get full text
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