Shifted Randomized Singular Value Decomposition
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Shifted Randomized Singular Value Decomposition. / Basirat, Ali.
2019.Research output: Working paper › Preprint › Research
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TY - UNPB
T1 - Shifted Randomized Singular Value Decomposition
AU - Basirat, Ali
PY - 2019/11/26
Y1 - 2019/11/26
N2 - 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.
AB - 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.
KW - stat.ML
KW - cs.LG
M3 - Preprint
BT - Shifted Randomized Singular Value Decomposition
ER -
ID: 366048878