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https://github.com/kisungyou/rdimtools
Dimension Reduction and Estimation Methods
https://github.com/kisungyou/rdimtools
dimension-estimation dimension-reduction manifold-learning r subspace-learning
Last synced: 7 days ago
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Dimension Reduction and Estimation Methods
- Host: GitHub
- URL: https://github.com/kisungyou/rdimtools
- Owner: kisungyou
- License: other
- Created: 2017-12-03T00:10:16.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2022-12-15T17:44:58.000Z (almost 2 years ago)
- Last Synced: 2023-10-20T21:00:47.648Z (about 1 year ago)
- Topics: dimension-estimation, dimension-reduction, manifold-learning, r, subspace-learning
- Language: R
- Homepage: https://kisungyou.com/Rdimtools
- Size: 90.4 MB
- Stars: 42
- Watchers: 3
- Forks: 10
- Open Issues: 2
-
Metadata Files:
- Readme: README.Rmd
- License: LICENSE
Awesome Lists containing this project
README
---
output: github_document
---
```{r setup, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
fig.align = "center",
out.width = "100%"
)
```
# Rdimtools 
[](https://cran.r-project.org/package=Rdimtools)
[](https://travis-ci.org/kisungyou/Rdimtools)
[](https://cran.r-project.org/package=Rdimtools)
```{r echo=FALSE, include=FALSE}
library(Rdimtools)
ndo = (sum(unlist(lapply(ls("package:Rdimtools"), startsWith, "do."))))
nest = (sum(unlist(lapply(ls("package:Rdimtools"), startsWith, "est."))))
```
**Rdimtools** is an R package for dimension reduction (DR) - including feature selection and manifold learning - and intrinsic dimension estimation (IDE) methods. We aim at building one of the *most comprehensive* toolbox available online, where current version delivers `r ndo` DR algorithms and `r nest` IDE methods.
The philosophy is simple, **the more we have at hands, the better we can play**.
## Elephant
Our logo characterizes the foundational nature of multivariate data analysis; we may be blind people wrangling the data to see an [elephant](https://en.wikipedia.org/wiki/Blind_men_and_an_elephant) to grasp an idea of what the data looks like with partial information from each algorithm.
## Installation
You can install a release version from CRAN:
```r
install.packages("Rdimtools")
```
or the development version from github:
```r
## install.packages("devtools")
devtools::install_github("kisungyou/Rdimtools")
```
## Minimal Example : Dimension Reduction
Here is an example of dimension reduction on the famous `iris` dataset. Principal Component Analysis (`do.pca`), Laplacian Score (`do.lscore`), and Diffusion Maps (`do.dm`) are compared, each from a family of algorithms for linear reduction, feature extraction, and nonlinear reduction.
```{r message=FALSE, warning=FALSE, fig.align='center', fig.width=7}
# load the library
library(Rdimtools)
# load the data
X = as.matrix(iris[,1:4])
lab = as.factor(iris[,5])
# run 3 algorithms mentioned above
mypca = do.pca(X, ndim=2)
mylap = do.lscore(X, ndim=2)
mydfm = do.dm(X, ndim=2, bandwidth=10)
# visualize
par(mfrow=c(1,3))
plot(mypca$Y, pch=19, col=lab, xlab="axis 1", ylab="axis 2", main="PCA")
plot(mylap$Y, pch=19, col=lab, xlab="axis 1", ylab="axis 2", main="Laplacian Score")
plot(mydfm$Y, pch=19, col=lab, xlab="axis 1", ylab="axis 2", main="Diffusion Maps")
```
## Minimal Example : Dimension Estimation
Swill Roll is a classic example of 2-dimensional manifold embedded in $\mathbb{R}^3$ and one of 11 famous model-based samples from `aux.gensamples()` function. Given the ground truth that $d=2$, let's apply several methods for intrinsic dimension estimation.
```{r message=FALSE, warning=FALSE, fig.align='center', fig.width=7, fig.height=3}
# generate sample data
set.seed(100)
roll = aux.gensamples(dname="swiss")
# we will compare 6 methods (out of 17 methods from version 1.0.0)
vecd = rep(0,5)
vecd[1] = est.Ustat(roll)$estdim # convergence rate of U-statistic on manifold
vecd[2] = est.correlation(roll)$estdim # correlation dimension
vecd[3] = est.made(roll)$estdim # manifold-adaptive dimension estimation
vecd[4] = est.mle1(roll)$estdim # MLE with Poisson process
vecd[5] = est.twonn(roll)$estdim # minimal neighborhood information
# let's visualize
plot(1:5, vecd, type="b", ylim=c(1.5,2.5),
main="true dimension is d=2",
xaxt="n",xlab="",ylab="estimated dimension")
xtick = seq(1,5,by=1)
axis(side=1, at=xtick, labels = FALSE)
text(x=xtick, par("usr")[3],
labels = c("Ustat","correlation","made","mle1","twonn"), pos=1, xpd = TRUE)
```
We can observe that all 5 methods we tested estimated the intrinsic dimension around $d=2$. It should be noted that the estimated dimension may not be integer-valued due to characteristics of each method.
## Acknowledgements
The logo icon is made by [Freepik](https://www.flaticon.com/authors/freepik/) from [www.flaticon.com](https://www.flaticon.com/).The rotating Swiss Roll image is taken from [Dinoj Surendran](https://people.cs.uchicago.edu/~dinoj/manifold/swissroll.html)'s website.