What is manifold learning in dimensionality reduction?

Manifold learning assumes high-dimensional data lies on a lower-dimensional curved surface.

Manifold learning techniques such as Isomap, LLE, and UMAP assume that although data exists in high-dimensional space, its intrinsic structure is lower-dimensional. These methods aim to preserve geometric relationships such as distances or local neighborhoods when projecting data into lower dimensions.