Manifold learning subsumes techniques conceived for problems where data of interest are assumed to lie on an embedded non-linear manifold within a higher-dimensional space.
Manifold Learning can be thought of as an attempt to generalize linear frameworks like PCA to be sensitive to non-linear structure in data. Though supervised variants exist, the typical manifold learning problem is unsupervised: it learns the high-dimensional structure of the data from the data itself, without the use of predetermined classifications.
Examples of Manifold Learning algorithms include:
- Isomap
- Locally Linear Embedding
- Hessian Eigenmapping
- Laplacian Eigenmaps
- Multi-dimensional Scaling (MDS)
- t-distributed Stochastic Neighbor Embedding (t-SNE)
Reference: http://scikit-learn.org/stable/modules/manifold.html and https://en.wikipedia.org/wiki/Nonlinear_dimensionality_reduction#Manifold_learning_algorithms
