rsparse

Statistical Learning on Sparse Matrices

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, <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://www.aclweb.org/anthology/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.

Total

148

Last month

148

Last week

83

Average per day

5

Daily downloads

Total downloads

Description file content

Package
rsparse
Type
Package
Title
Statistical Learning on Sparse Matrices
Version
0.3.3.1
Maintainer
Dmitriy Selivanov
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 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(, ) 2) Factorization Machines via SGD, as per Rendle (2010, ) 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, ) 2) Maximum-Margin Matrix Factorization via ALS, paper by Rennie, Srebro (2005, ) 3) Fast Truncated Singular Value Decomposition (SVD), Soft-Thresholded SVD, Soft-Impute matrix completion via ALS - paper by Hastie, Mazumder et al. (2014, ) 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, ) 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.
License
GPL (>= 2)
Encoding
UTF-8
LazyData
true
ByteCompile
true
Depends
R (>= 3.1.0), methods
Imports
Matrix (>= 1.2), Rcpp (>= 0.11), mlapi (>= 0.1.0), data.table (>= 1.10.0), float (>= 0.2-2), RhpcBLASctl, lgr (>= 0.2)
LinkingTo
Rcpp, RcppArmadillo (>= 0.9.100.5.0)
Suggests
testthat, covr
URL
BugReports
https://github.com/dselivanov/rsparse/issues
RoxygenNote
6.1.0
NeedsCompilation
yes
Packaged
2019-04-14 10:23:29 UTC; dselivanov
Author
Dmitriy Selivanov [aut, cre, cph] (), Drew Schmidt [ctb] (configure script for BLAS, LAPACK detection), Wei-Chen Chen [ctb] (configure script and work on linking to float package)
Repository
CRAN
Date/Publication
2019-04-14 20:13:09 UTC

install.packages('rsparse')

0.3.3.1

2 months ago

https://github.com/dselivanov/rsparse

Dmitriy Selivanov

GPL (>= 2)

Depends on

R (>= 3.1.0), methods

Imports

Matrix (>= 1.2), Rcpp (>= 0.11), mlapi (>= 0.1.0), data.table (>= 1.10.0), float (>= 0.2-2), RhpcBLASctl, lgr (>= 0.2)

Suggests

testthat, covr

Discussions