CMF: Collective matrix factorization
Version 1.0

Collective matrix factorization (CMF) finds joint low-rank representations for a collection of matrices with shared row or column entities. This code learns variational Bayesian approximation for CMF, supporting multiple likelihood potentials and missing data, while identifying both factors shared by multiple matrices and factors private for each matrix.

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AuthorArto Klami and Lauri Väre
Date of publication2014-03-25 14:26:42
MaintainerArto Klami <arto.klami@cs.helsinki.fi>
LicenseGPL (>= 2)
Version1.0
Package repositoryView on CRAN
InstallationInstall the latest version of this package by entering the following in R:
install.packages("CMF")

Man pages

CMF: Collective Matrix Factorization
CMF-package: Collective Matrix Factorization (CMF)
getCMFopts: Default options for CMF
matrix_to_triplets: Conversion from matrix to coordinate/triplet format
predictCMF: Predict with CMF
triplets_to_matrix: Conversion from triplet/coordinate format to matrix

Functions

CMF Man page Source code
CMF-package Man page
getCMFopts Man page Source code
matrix_to_triplets Man page Source code
p_check_sparsity Source code
p_covUsparse Source code
p_gradUsparse Source code
p_gradUsparseWrapper Source code
p_updateMean Source code
p_updatePseudoData Source code
p_updateTau Source code
predictCMF Man page Source code
triplets_to_matrix Man page Source code

Files

src
src/Makevars
src/helper.cpp
src/Makevars.win
src/RcppExports.cpp
NAMESPACE
R
R/RcppExports.R
R/CMF.R
MD5
DESCRIPTION
man
man/getCMFopts.Rd
man/CMF.Rd
man/predictCMF.Rd
man/matrix_to_triplets.Rd
man/CMF-package.Rd
man/triplets_to_matrix.Rd
CMF documentation built on May 20, 2017, 3:11 a.m.