knitr::opts_chunk$set(comment = "#>", warning = FALSE, eval = TRUE, message = FALSE, collapse = TRUE) library(eimpute)
Matrix completion is a procedure for imputing the missing elements in matrices by using the information of observed elements. This procedure can be visualized as:
Matrix completion has attracted a lot of attention, it is widely applied in:
A computationally efficient R package, eimpute is developed for matrix completion. In eimpute, matrix completion problem is solved by iteratively performing low-rank approximation and data calibration, which enjoy two admirable advantages:
Compare eimpute and softimpute in systhesis datasets $X_{m \times m}$ with $p$ proportion missing observations. The square matrix $X_{m \times m}$ is generated by $X = UV + \epsilon$, where $U$ and $V$ are $m \times r$, $r \times n$ matrices whose entries are $i.i.d.$ sampled standard normal distribution, $\epsilon \sim N(0, r/3)$.
In high dimension case, als method in softimpute is a little faster than eimpute in low proportion of missing observations, as the proportion of missing observations increase, rsvd method in eimpute have a better performance than softimpute in time cost and test error. Compare with two method in *eimpute, rsvd method is better than tsvd in time cost.
Install the stable version from CRAN:
install.packages("eimpute")
Install the development version from github:
library(devtools) install_github("Mamba413/eimpute", build_vignettes = TRUE)
We start with a toy example. Let us generate a small matrix with some values missing via incomplete.generator function.
m <- 6 n <- 5 r <- 3 x_na <- incomplete.generator(m, n, r) x_na
Use eimpute function to impute missing values.
x_impute <- eimpute(x_na, r) x_impute[["x.imp"]]
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.