README.Rmd

---
output: github_document
---

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```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
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)
```

# fastadi

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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`](https://cran.r-project.org/package=softImpute) package), truncated SVD, maximum margin matrix factorization, and weighted regularized matrix factorization (implemented in the [`rsparse`](https://github.com/rexyai/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.

## Installation

You can install the released version from [CRAN](https://cran.r-project.org/) with:

``` r
install.packages("fastadi")
```

You can install the development version from [GitHub](https://github.com/) with:

``` r
# install.packages("devtools")
devtools::install_github("RoheLab/fastadi")
```

## Example usage

Here we embed users and items in the MovieLens 100K dataset.

```{r}
library(fastadi)

mf <- adaptive_impute(ml100k, rank = 3L, max_iter = 5L)
```

```{r}
mf
```

Note that the vignettes are currently scratch work for reference by the developers and are not yet ready for general consumption.

## References

1. Alex Hayes and Karl Rohe. "Finding Topics in Citation Data". 2022+

2. 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.

3. ———. “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.

4. 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](https://github.com/chojuhee/hello-world).