套件：r-cran-rsvd（1.0.5-1）
Randomized Singular Value Decomposition
Low-rank matrix decompositions are fundamental tools and widely used for data analysis, dimension reduction, and data compression. Classically, highly accurate deterministic matrix algorithms are used for this task. However, the emergence of large-scale data has severely challenged our computational ability to analyze big data. The concept of randomness has been demonstrated as an effective strategy to quickly produce approximate answers to familiar problems such as the singular value decomposition (SVD). The rsvd package provides several randomized matrix algorithms such as the randomized singular value decomposition (rsvd), randomized principal component analysis (rpca), randomized robust principal component analysis (rrpca), randomized interpolative decomposition (rid), and the randomized CUR decomposition (rcur). In addition several plot functions are provided. The methods are discussed in detail by Erichson et al. (2016) <arXiv:1608.02148>.
其他與 r-cran-rsvd 有關的套件
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- dep: r-api-4.0
- 本虛擬套件由這些套件填實： r-base-core
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- dep: r-base-core (>= 4.1.1-2)
- GNU R core of statistical computation and graphics system
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- dep: r-cran-matrix
- GNU R package of classes for dense and sparse matrices
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- rec: r-cran-ggplot2
- implementation of the Grammar of Graphics
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- rec: r-cran-plyr
- tools for splitting, applying and combining data
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- rec: r-cran-scales
- Scale functions for visualization
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- rec: r-cran-testthat
- GNU R testsuite