Package: CMF 1.0.3.99
CMF: Collective Matrix Factorization
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) <arxiv:1312.5921>. 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) <https://research.cs.aalto.fi/pml/online-papers/klami13a.pdf> and Virtanen et al. (2012) <http://proceedings.mlr.press/v22/virtanen12.html> for details on Bayesian CCA and GFA, respectively.
Authors:
CMF_1.0.3.99.tar.gz
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CMF_1.0.3.99.tgz(r-4.4-x86_64)CMF_1.0.3.99.tgz(r-4.4-arm64)CMF_1.0.3.99.tgz(r-4.3-x86_64)CMF_1.0.3.99.tgz(r-4.3-arm64)
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CMF.pdf |CMF.html✨
CMF/json (API)
NEWS
# Install 'CMF' in R: |
install.packages('CMF', repos = c('https://cyianor.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/cyianor/cmf/issues
bayesian-inferencecmfdata-integration
Last updated 2 years agofrom:93d52dbd4a. Checks:OK: 9. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 03 2024 |
R-4.5-win-x86_64 | OK | Nov 03 2024 |
R-4.5-linux-x86_64 | OK | Nov 03 2024 |
R-4.4-win-x86_64 | OK | Nov 03 2024 |
R-4.4-mac-x86_64 | OK | Nov 03 2024 |
R-4.4-mac-aarch64 | OK | Nov 03 2024 |
R-4.3-win-x86_64 | OK | Nov 03 2024 |
R-4.3-mac-x86_64 | OK | Nov 03 2024 |
R-4.3-mac-aarch64 | OK | Nov 03 2024 |
Exports:CMFgetCMFoptsmatrix_to_tripletspredictCMFtriplets_to_matrix
Dependencies:cpp11
Readme and manuals
Help Manual
Help page | Topics |
---|---|
Collective Matrix Factorization (CMF) | CMF-package |
Collective Matrix Factorization | CMF |
Default options for CMF | getCMFopts |
Conversion from matrix to coordinate/triplet format | matrix_to_triplets |
Internal function for checking whether the input is in the right format | p_check_sparsity |
Predict with CMF | predictCMF |
Conversion from triplet/coordinate format to matrix | triplets_to_matrix |