1. A global geometric framework for nonlinear dimensionality reduction;Tenenbaum;Science,2000
2. Nonlinear dimensionality reduction by locally linear embedding;Roweis;Science,2000
3. M. Belkin, P. Niyogi, Using manifold structure for partially labelled classification, Advances in NIPS 15.
4. When does isomap recover natural parameterization of families of articulated images?;Donoho,2002
5. Hessian eigenmaps: new locally linear embedding techniques for high-dimensional data;Donoho,2003