In this vignette, we will demonstrate basic functionalities of Rdimtools package by walking through from installation to analysis with the famous iris dataset.
Rdimtools can be installed in two handy options. A release version from CRAN can be installed
or a development version is available from GitHub with
## install.packages("devtools") devtools::install_github("kisungyou/Rdimtools")
Now we are ready to go by loading the library.
As of current release version 1.0.9, there are 17 intrinsic dimension estimation (IDE) algorithms. In the following example, we will only show 5 methods’ performance.
# load the iris data X = as.matrix(iris[,1:4]) lab = as.factor(iris[,5]) # we will compare 5 methods (out of 17 methods from version 1.0.0) vecd = rep(0,5) vecd = est.Ustat(X)$estdim # convergence rate of U-statistic on manifold vecd = est.correlation(X)$estdim # correlation dimension vecd = est.made(X)$estdim # manifold-adaptive dimension estimation vecd = est.mle1(X)$estdim # MLE with Poisson process vecd = est.twonn(X)$estdim # minimal neighborhood information # let's visualize plot(1:5, vecd, type="b", ylim=c(1.5,3.5), main="estimating dimension of iris data", xaxt="n",xlab="",ylab="estimated dimension") xtick = seq(1,5,by=1) axis(side=1, at=xtick, labels = FALSE) text(x=xtick, par("usr"), labels = c("Ustat","correlation","made","mle1","twonn"), pos=1, xpd = TRUE)
As the true dimension is not known for a given dataset, different methods bring about heterogeneous estimates. That’s why we deliver 17 methods at an unprecedented scale to provide a broader basis for your decision.
Currently, Rdimtools (ver 1.0.9) delivers 141 dimension reduction/feature selection/manifold learning algorithms. Among a myriad of methods, we compare Principal Component Analysis (
do.pca), Laplacian Score (
do.lscore), and Diffusion Maps (
do.dm) are compared, each from a family of algorithms for linear reduction, feature extraction, and nonlinear reduction.
# run 3 algorithms mentioned above mypca = do.pca(X, ndim=2) mylap = do.lscore(X, ndim=2) mydfm = do.dm(X, ndim=2, bandwidth=10) # extract embeddings from each method Y1 = mypca$Y Y2 = mylap$Y Y3 = mydfm$Y # visualize par(mfrow=c(1,3)) plot(Y1, pch=19, col=lab, xlab="axis 1", ylab="axis 2", main="PCA") plot(Y2, pch=19, col=lab, xlab="axis 1", ylab="axis 2", main="Laplacian Score") plot(Y3, pch=19, col=lab, xlab="axis 1", ylab="axis 2", main="Diffusion Maps")
As the figure above shows, in general, different algorithms show heterogeneous nature of the data. We hope Rdimtools be a valuable toolset to help practitioners and scientists discover many facets of the data.