Shifted Randomized Singular Value Decomposition
Publikation: Working paper › Preprint › Forskning
Dokumenter
- 1911.11772v2
600 KB, PDF-dokument
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.
Originalsprog | Engelsk |
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Status | Udgivet - 26 nov. 2019 |
Eksternt udgivet | Ja |
- stat.ML, cs.LG
Forskningsområder
ID: 366048878