| Name : R-rsparse
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| Version : 0.5.2
| Vendor : obs://build_opensuse_org/devel:languages:R
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| Release : lp154.1.4
| Date : 2024-09-12 11:02:08
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| Group : Development/Libraries/Other
| Source RPM : R-rsparse-0.5.2-lp154.1.4.src.rpm
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| Size : 1.34 MB
| |
| Packager : https://www_suse_com/
| |
| Summary : Statistical Learning on Sparse Matrices
|
Description :
Implements many algorithms for statistical learning on sparse matrices
- matrix factorizations, matrix completion, elastic net regressions,
factorization machines. Also \'rsparse\' enhances \'Matrix\' package by
providing methods for multithreaded < sparse, dense> matrix products and
native slicing of the sparse matrices in Compressed Sparse Row (CSR)
format. List of the algorithms for regression problems: 1) Elastic Net
regression via Follow The Proximally-Regularized Leader (FTRL)
Stochastic Gradient Descent (SGD), as per McMahan et al(,
< doi:10.1145/2487575.2488200>) 2) Factorization Machines via SGD, as
per Rendle (2010, < doi:10.1109/ICDM.2010.127>) List of algorithms for
matrix factorization and matrix completion: 1) Weighted Regularized
Matrix Factorization (WRMF) via Alternating Least Squares (ALS) - paper
by Hu, Koren, Volinsky (2008, < doi:10.1109/ICDM.2008.22>) 2)
Maximum-Margin Matrix Factorization via ALS, paper by Rennie, Srebro
(2005, < doi:10.1145/1102351.1102441>) 3) Fast Truncated Singular Value
Decomposition (SVD), Soft-Thresholded SVD, Soft-Impute matrix
completion via ALS - paper by Hastie, Mazumder et al. (2014,
< doi:10.48550/arXiv.1410.2596>) 4) Linear-Flow matrix factorization,
from \'Practical linear models for large-scale one-class collaborative
filtering\' by Sedhain, Bui, Kawale et al (2016, ISBN:978-1-57735-770-4)
5) GlobalVectors (GloVe) matrix factorization via SGD, paper by
Pennington, Socher, Manning (2014,
< https://aclanthology.org/D14-1162/>) Package is reasonably fast and
memory efficient - it allows to work with large datasets - millions of
rows and millions of columns. This is particularly useful for
practitioners working on recommender systems.
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RPM found in directory: /packages/linux-pbone/ftp5.gwdg.de/pub/opensuse/repositories/devel:/languages:/R:/autoCRAN/15.4/x86_64 |
