Randomized Matrix Decompositions Using R

Randomized Matrix Decompositions Using R

Matrix decompositions are fundamental tools in the area of applied mathematics, statistical computing, and machine learning. In particular, low-rank matrix decompositions are vital, and widely used for data analysis, dimensionality reduction, and data compression. Massive datasets, however, pose a c...

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Bibliographic Details
Main Authors: N. Benjamin Erichson, Sergey Voronin, Steven L. Brunton, J. Nathan Kutz
Format: Article
Language:English
Published: Foundation for Open Access Statistics 2019-06-01
Series:Journal of Statistical Software
Subjects:
dimension reduction
randomized algorithm
low-rank approximations
singular value decomposition
principal component analysis
cur decomposition
r
Online Access:https://www.jstatsoft.org/index.php/jss/article/view/2788

Internet

https://www.jstatsoft.org/index.php/jss/article/view/2788

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