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Spectral methods for manifold learning and clustering typically construct a graph weighted with affinities from a dataset and compute eigenvectors of a graph Laplacian. With large datasets, the eigendecomposition is too expensive, and is usually approximated by solving for a smaller graph defined on a subset of the points (landmarks) and then applying the Nystrom formula to estimate the eigenvectors over all points. This has the problem that the affinities between landmarks do not benefit from the remaining points and may poorly represent the data if using few landmarks. We introduce a modified spectral problem that uses all data points by constraining the latent projection of each point to be a local linear function of the landmarks' latent projections. This constructs a new affinity matrix between landmarks that preserves manifold structure even with few landmarks, allows one to reduce the eigenproblem size, and defines a fast, nonlinear out-of-sample mapping.

Locally Linear Landmarks for Large-Scale Manifold Learning