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fastadi implements the AdaptiveImpute
matrix completion algorithm. fastadi is a self-tuning
alternative to algorithms such as SoftImpute (implemented
in the softImpute
package), truncated SVD, maximum margin matrix factorization, and
weighted regularized matrix factorization (implemented in the rsparse
package). In simulations fastadi often outperforms
softImpute by a small margin.
You may find fastadi useful if you are developing
embeddings for sparsely observed data, if you are working in natural
language processing, or building a recommendation system.
You can install the released version from CRAN with:
install.packages("fastadi")You can install the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("RoheLab/fastadi")Here we embed users and items in the MovieLens 100K dataset.
library(fastadi)
#> Loading required package: LRMF3
#> Loading required package: Matrix
mf <- adaptive_impute(ml100k, rank = 3L, max_iter = 5L)
#> Warning:
#> Reached maximum allowed iterations. Returning early.mf
#>
#> Adaptively Imputed Low Rank Matrix Factorization
#> ------------------------------------------------
#>
#> Rank: 3
#>
#> Rows: 943
#> Cols: 1682
#>
#> d[rank]: 467.486
#> alpha: 144.663
#>
#> Components
#>
#> u: 943 x 3 [matrix]
#> d: 3 [numeric]
#> v: 1682 x 3 [matrix]Note that the vignettes are currently scratch work for reference by the developers and are not yet ready for general consumption.
Alex Hayes and Karl Rohe. “Finding Topics in Citation Data”. 2022+
Cho, Juhee, Donggyu Kim, and Karl Rohe. “Asymptotic Theory for Estimating the Singular Vectors and Values of a Partially-Observed Low Rank Matrix with Noise.” Statistica Sinica, 2018. https://doi.org/10.5705/ss.202016.0205.
———. “Intelligent Initialization and Adaptive Thresholding for Iterative Matrix Completion: Some Statistical and Algorithmic Theory for Adaptive-Impute.” Journal of Computational and Graphical Statistics 28, no. 2 (April 3, 2019): 323–33. https://doi.org/10.1080/10618600.2018.1518238.
Mazumder, Rahul, Trevor Hastie, and Robert Tibshirani. “Spectral Regularization Algorithms for Learning Large Incomplete Matrices.” Journal of Machine Learning Research, 2010. https://web.stanford.edu/~hastie/Papers/mazumder10a.pdf.
You can find the original implementation accompanying these papers here.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.