I often use PCA(principal component analysis) to reduce dimension.
I do PCA using Python sklearn or R language.
Basic function of R “biplot” makes 2D chart.
It’s easy way to make biplot.
Today I found cool library of R, named “pca3d”.
Install is easy! Just type following command.
install.packages("pca3d")
Now make chart.
I used iris data set for test.
> library(pca3d) > data(iris) > pca <- prcomp(iris[,-5], scale.=TRUE) > pca3d(pca, group=iris[,5], biplot=TRUE) [1] 0.06599283 0.05354630 0.02004088 Creating new device
Enter pca3d command, X quarts was launched and I got 3d biplot.
The chart can move.
Hmm, 3D chart is useful to check the distribution of datapoint, but… I like 2D biplot. ;-)
If reader who is interested in the library, please check following site.
This is very useful as a clustering validation/representation technique, but I think that’s it