You should consider that a principal component assumes the linear transformations of variables, but there are other non-linear dimensionality reduction techniques.
For me, one of the most interesting techniques is the t-SNE developed by Laurens van der Maaten, who says this:
"As a sanity check, try running PCA on your data to reduce it to two dimensions. If this also gives bad results, then maybe there is not very much nice structure in your data in the first place. If PCA works well but t-SNE doesn’t, I am fairly sure you did something wrong."
Let’s see an example of how t-SNE is applied on our dataset. As usual, it is recommended that you fix a seed:
set.seed(1234)
We will need to use the Rtsne package. ...
