Manifold learning

Manifold learning
Manifold learning, nonlinear dimensionality reduction, low-dimensional structure in high-dimensional data

Manifold learning refers to nonlinear dimensionality reduction techniques that seek to uncover low-dimensional structure in high-dimensional data. Key characteristics:

Applications include:

Challenges include setting appropriate hyperparameters, preserving local and global structure, and determining intrinsic dimensionality.

Overall, manifold learning provides key tools for extracting nonlinear low-dimensional representations spanned by real-world high-dimensional data.

See also: