In this section, we'll discuss the widespread non-linear methods and algorithms that are used for dimension reduction.
Exploring non-linear methods for dimension reduction
Kernel PCA
Classic PCA is a linear projection method that works well if the data is linearly separable. However, in the case of linearly non-separable data, a non-linear approach is required. The basic idea of working with linearly inseparable data is to project it into a space with a larger number of dimensions, where it becomes linearly separable. We can choose a non-linear mapping function, 
, so that the sample mapping, x, can be written as 
. This is called the kernel function. The term kernel describes a function that calculates the scalar product...
