Overview

Description

These methods transform the features into a lower-dimensional space while retaining as much information as possible.

Manifold learning methods assume that data lies on a curved manifold within a higher-dimensional space. These techniques are designed to uncover complex, nonlinear structures in the data.

Manifold Learning Methods are best for data with complex, nonlinear structures. They focus on preserving local and global geometric properties of the data manifold.