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CMF
View on CRAN: Click
here
Download and install CMF package within the R console
Install from CRAN:
install.packages("CMF")
Install from Github:
library("remotes")
install_github("cran/CMF")
Install by package version:
library("remotes")
install_version("CMF", "1.0.3")
Attach the package and use:
library("CMF")
Maintained by
Felix Held
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2020-03-12
Latest Update: 2022-08-09
Description:
Collective matrix factorization (CMF) finds joint low-rank
representations for a collection of matrices with shared row or column
entities. This code learns a 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. For further details on the method see
Klami et al. (2014) .
The package can also be used to learn Bayesian canonical correlation
analysis (CCA) and group factor analysis (GFA) models, both of which are
special cases of CMF. This is likely to be useful for people looking for
CCA and GFA solutions supporting missing data and non-Gaussian likelihoods.
See Klami et al. (2013)
and Virtanen et al. (2012)
for details on Bayesian CCA and GFA, respectively.
How to cite:
Felix Held (2020). CMF: Collective Matrix Factorization. R package version 1.0.3, https://cran.r-project.org/web/packages/CMF
Previous versions and publish date:
Other packages that cited CMF R package
View CMF citation profile
Other R packages that CMF depends,
imports, suggests or enhances
Functions, R codes and Examples using
the CMF R package
Some associated functions: CMF-package . CMF . getCMFopts . matrix_to_triplets . p_check_sparsity . p_gradUsparseWrapper . predictCMF . triplets_to_matrix .
Some associated R codes: CMF.R . cpp11.R . Full CMF package functions and examples
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