The Kernel PCA is an algorithm that not only keeps the main spirit of PCA as it is, but goes a step further to make use of the kernel trick so that it is operational for non-linear data:
- Let's define the covariance matrix of the data in the feature space, which is the product of the mapping function and the transpose of the mapping function:
It is similar to the one we used for PCA.
- The next step is to solve the following equation so that we can compute principal components:
Here, CF is the covariance matrix of the data in feature ...