Kernel PCA is one of the variations of ** principal component analysis** in machine learning that uses kernel methods to perform the initially linear operations of principal component analysis. In this article, I will take you through an introduction to Kernel PCA in Machine Learning and its implementation using Python.

## What is Kernel PCA?

In machine learning, Kernel PCA is one of the variations of principal component analysis in which we use kernel methods to perform principal component analysis with nonlinearly separable datasets. The Kernel approach of Principal Component Analysis is very similar to the standard one but with a different processing step.

Contrary to what you might expect, a nonlinear low-dimensional dataset can often become linearly separable if projected onto a special high-dimensional space. The Kernel approach does the same, it achieves this goal without having to use very hard, non-linear operations.

When working on a machine learning problem, when a classic PCA approach fails to capture the nonlinear dependency of existing principal components, you should use the Kernal PCA approach. When implementing this method, we assume that there are principal components in the high-dimensional space whose explained variance is negligible concerning the others.

## Kernel PCA using Python

Hope you now understand the idea behind using a kernel approach for the task of principal component analysis and how it is different from the standard approach. Now let’s see how we can use the scikit-learn library to implement Kernel PCA using Python:

### Summary

The Kernel method of Principal Component Analysis is a powerful approach when the dataset is made up of items that may be a function of the principal components, but we are unable to determine a linear relationship between them. Hope you liked this article on an introduction to the Kernel approach of Principal Component Analysis in Machine Learning and its implementation using Python. Please feel free to ask your valuable questions in the comments section below.