Manifold learning, aka manifold estimation, are methods that extract the geometric structure of data.
Manifold learning is also viewed as a subset of topological data analysis [@Wasserman2018].
Isometric embedding: multi-dimensional scaling (MDS) [@Schoenberg1937]; ISOMAP [@Tenenbaum2000].
Low Lipschitz distortion, randomized embedding [@Johnson and Lindenstrauss, 1984].
All kernel-based manifold learning methods are all special cases of kernel principal component analysis (kernel PCA) [@Ham2004].
local linear embedding (LLE) [@Roweis2000]
local tangent space alignment (LTSA) [@ZhangZY2004]
Laplacian eigenmaps [@Belkin2003]
Hessian eigenmaps [@Donoho2003]
diffusion maps [@Coifman2005a; @Coifman2006a]
geometric harmonics [@Coifman2005b; @Coifman2006b]