Performing kernel principal component analysis

PCA is good at reducing the number of dimensions, but it works in a linear manner. If the data is not organized in a linear fashion, PCA fails to do the required job. This is where kernel PCA enters the picture.

Getting ready

In this recipe, we will see how we can perform a kernel PCA on the input data and compare the result to how PCA performs on the same data.

How to do it...

Let's see how we can perform a kernel PCA:

1. Create a new Python file and import the following packages (the full code is given in the kpca.py...