1. Quantifying the neighborhood preservation of self-organizing maps;Bauer;IEEE Trans. Neural Networks,1992
2. Laplacian eigenmaps for dimensionality reduction and data representation;Belkin;Neural Comput.,2003
3. Bengio, Y., Vincent, P., Paiement, J.-F., Delalleau, O., Ouimet, M., Le Roux, N., 2003. Spectral clustering and kernel PCA are learning eigenfunctions. Tech. rep. 1239, Département d’Informatique et Recherche Opérationnelle, Université de Montréal, Montréal.
4. Bernstein, M., de Silva, V., Langford, J., Tenenbaum, J., 2000. Graph approximations to geodesics on embedded manifolds. Tech. rep., Stanford University, Palo Alto, CA.
5. Brand, M., Huang, K., 2003. A unifying theorem for spectral embedding and clustering. In: Bishop, C., Frey, B. (Eds.), Proc. Internat. Workshop on Artificial Intelligence and Statistics (AISTATS’03).