Shifted Randomized Singular Value Decomposition
Machine Learning
2019-12-02 v2 Machine Learning
Abstract
We extend the randomized singular value decomposition (SVD) algorithm \citep{Halko2011finding} to estimate the SVD of a shifted data matrix without explicitly constructing the matrix in the memory. With no loss in the accuracy of the original algorithm, the extended algorithm provides for a more efficient way of matrix factorization. The algorithm facilitates the low-rank approximation and principal component analysis (PCA) of off-center data matrices. When applied to different types of data matrices, our experimental results confirm the advantages of the extensions made to the original algorithm.
Keywords
Cite
@article{arxiv.1911.11772,
title = {Shifted Randomized Singular Value Decomposition},
author = {Ali Basirat},
journal= {arXiv preprint arXiv:1911.11772},
year = {2019}
}