Kernel Principal Component Analysis (KPCA) is a non-linear dimensionality reduction technique. It is an extension of Principal Component Analysis (PCA) - which is a linear dimensionality reduction technique - using kernel methods.
Read this article to understand Kernel Principal Component Analysis (KPCA) in depth
The key idea of KPCA relies on the intuition that many datasets, which are not linearly separable in their space, can be made linearly separable by projecting them into a higher dimensional space. The added dimensions are just simple arithmetic operations performed on the original data dimensions.
Have a doubt or thought? Join the discussion now
This is a companion discussion topic for the original entry at http://iq.opengenus.org/kernal-principal-component-analysis/