R package rsvd. Randomized singular value decomposition (rsvd) is a very fast probabilistic algorithm that can be used to compute the near optimal low-rank singular value decomposition of massive data sets with high accuracy. SVD plays a central role in data analysis and scientific computing. SVD is also widely used for computing (randomized) principal component analysis (PCA), a linear dimensionality reduction technique. Randomized PCA (rpca) uses the approximated singular value decomposition to compute the most significant principal components. This package also includes a function to compute (randomized) robust principal component analysis (RPCA). In addition several plot functions are provided.
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References in zbMATH (referenced in 7 articles , 1 standard article )
Showing results 1 to 7 of 7.
- Duersch, Jed A.; Gu, Ming: Randomized projection for rank-revealing matrix factorizations and low-rank approximations (2020)
- Erichson, N. Benjamin; Zheng, Peng; Manohar, Krithika; Brunton, Steven L.; Kutz, J. Nathan; Aravkin, Aleksandr Y.: Sparse principal component analysis via variable projection (2020)
- Alla, Alessandro; Kutz, J. Nathan: Randomized model order reduction (2019)
- Bjarkason, Elvar K.: Pass-efficient randomized algorithms for low-rank matrix approximation using any number of views (2019)
- N. Benjamin Erichson, Sergey Voronin, Steven L. Brunton, J. Nathan Kutz: Randomized Matrix Decompositions Using R (2019) not zbMATH
- Buhr, Andreas; Smetana, Kathrin: Randomized local model order reduction (2018)
- Bouwmans, Thierry; Sobral, Andrews; Javed, Sajid; Jung, Soon Ki; Zahzah, El-Hadi: Decomposition into low-rank plus additive matrices for background/foreground separation: a review for a comparative evaluation with a large-scale dataset (2017)