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Matrix decompositions using sub-Gaussian random matrices
Yariv Aizenbud
*
,
Amir Averbuch
*
Corresponding author for this work
Department of Theoretical Mathematics
School of Computer Science
Research output
:
Contribution to journal
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Article
›
peer-review
6
Scopus citations
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Keyphrases
Singular Value Decomposition
100%
Error Bound
100%
Sub-Gaussian
100%
Matrix Decomposition
100%
Gaussian Random Matrix
100%
Tight
50%
High Probability
50%
Approximation Algorithms
50%
Error Rate
50%
Linear Space
50%
Random Projection
50%
Singular Values
50%
Decomposition Algorithm
50%
Asymptotic Complexity
50%
LU Decomposition
50%
Projection Format
50%
Sub-Gaussian Matrices
50%
Mathematics
Probability Theory
100%
Matrix (Mathematics)
100%
Error Bound
100%
Singular Value Decomposition
100%
Matrix Decomposition
100%
Gaussian Random Matrix
100%
Gaussian Distribution
50%
Approximates
50%
Asymptotics
50%
Null
50%
Linear Space
50%
Error Rate
50%
Singular Value
50%
Decomposition Algorithms
50%
LU Decomposition
50%
Computer Science
Singular Value
100%
Decomposition Matrix
100%
Approximation (Algorithm)
33%
Approximation Algorithms
33%
Random Projection
33%
Asymptotic Complexity
33%
Projection Type
33%