Dimensionality Reduction

Principal component analysis (PCA)

It is an unsupervised linear transformation technique that is widely used across different fields, most prominently for dimensionality reduction.

Linear Discriminant Analysis (LDA)

It can be used as a technique for feature extraction to increase the computational efficientcy and reduce the degreee of over-fitting due to the curse of dimensionality in nonregularized models.

Kernel principal component analysis

It will transform data that is not linearly separable onto a new, lower-dimensional subspace that is suitable for linear classifiers

Downside: This approach is computational expesnsive, but it can be overcome by kernel trick.

Radial Basis Function (RBF)