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 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: Matrix
#> Loading required package: LRMF3
mf <- adaptive_impute(ml100k, rank = 3L, max_iter = 5L)
#> INFO [2020-10-05 11:27:08] Use svd initialization.
#> INFO [2020-10-05 11:27:08] Done initializing.
#> INFO [2020-10-05 11:27:08] Beginning AdaptiveImpute (max 5 iterations).
#> INFO [2020-10-05 11:27:08] Checking convergence every 1 iteration(s).
#> INFO [2020-10-05 11:27:09] Iter 1 complete. delta = 0.22312337, alpha = 184.71
#> INFO [2020-10-05 11:27:09] Iter 2 complete. delta = 0.05159124, alpha = 154.411
#> INFO [2020-10-05 11:27:10] Iter 3 complete. delta = 0.02125919, alpha = 135.61
#> INFO [2020-10-05 11:27:10] Iter 4 complete. delta = 0.01114668, alpha = 122.308
#> INFO [2020-10-05 11:27:11] Iter 5 complete. delta = 0.00669206, alpha = 112.354
#> Warning:
#> Reached maximum allowed iterations. Returning early.mf
#>
#> Adaptively Imputed Low Rank Matrix Factorization
#> ------------------------------------------------
#>
#> Rank: 3
#>
#> Rows: 943
#> Cols: 1682
#>
#> d[rank]: 467.762
#> alpha: 112.354
#>
#> 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.
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Cho, J., Kim, D. & Rohe, K. Asymptotic Theory for Estimating the Singular Vectors and Values of a Partially-observed Low Rank Matrix with Noise. arXiv:1508.05431 [stat] (2015). http://arxiv.org/abs/1508.05431
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Cho, J., Kim, D. & Rohe, K. Intelligent Initialization and Adaptive Thresholding for Iterative Matrix Completion; Some Statistical and Algorithmic Theory for Adaptive-Impute. Journal of Computational and Graphical Statistics 1–26 (2018) doi:10.1080/10618600.2018.1518238. https://amstat.tandfonline.com/doi/abs/10.1080/10618600.2018.1518238
You can find the original implementation accompanying these papers here.