Baker C. (1977). The numerical treatment of integral equations.
Chung F. (1997). Spectral graph theory.
Cox T, Cox M. (1994). Multidimensional scaling.
Donoho D, Grimes C. (2003). Hessian eigenmaps: new locally linear embedding techniques for high-dimensional data Tech Rep 2003-08 Stanford University Department of Statistics.
Frey B, Rosales R. (2003). Learning generative models of affinity matrices Proc 19th Annual Conf Uncertainty in Artificial Intelligence.
Gower JC. (1968). Adding a point to vector diagrams in multivariate analysis Biometrika. 55
Koltchinskii V, Gine E. (2000). Random matrix approximation of spectra of integral operators Bernoulli. 6
Kreyszig E. (1990). Introductory functional analysis with applications.
Lee D, Scholkopf B, Mika S, Ham J. (2003). A kernel view of the dimensionality reduction of manifolds Tech Rep TR-110, Max Planck Institute for Biological Cybernetics.
Niyogi P, Belkin M. (2003). Laplacian eigenmaps for dimensionality reduction and data representation Neural Comput. 15
Roweis S, Saul L. (2002). Think globally, fit locally: Unsupervised learning of low dimensional manifolds J Mach Learn Res. 4
Roweis ST, Saul LK. (2000). Nonlinear dimensionality reduction by locally linear embedding. Science (New York, N.Y.). 290 [PubMed]
Scholkopf B, Smola A, Muller KR. (1996). Nonlinear component analysisas a kernel eigen value problem Tech Rep 44, Max Planck Institute for Biological Cybernetics.
Scholkopf B, Smola A, Muller KR. (1998). Nonlinear component analysis as a kernel eigenvalue problem Neural Comput. 10
Scholkopf B, Smola A, Muller KR. (1999). Kernel principal component analysis Advances in kernel methods-Support vector learning.
Shawe-taylor J, Cristianini N, Kandola J. (2002). On the concentration of spectral properties Advances in neural information processing systems. 14
Shawe-taylor J, Williams C. (2003). The stability of kernel principal components analysis and its relation to the process eigen spectrum Advances in neural information processing systems. 15
Shi J, Malik J. (1997). Normalized cuts and image segmentation Proc IEEE Conf Computer Vision and Pattern Recognition.
Spielman D, Teng S. (1996). Spectral partitionning works: planar graphs and finite element meshes Proc 37th Ann Symposium on Foundations of Computer Science.
Strang G. (1988). Linear algebra and its application.
Tenenbaum JB, de Silva V, Langford JC. (2000). A global geometric framework for nonlinear dimensionality reduction. Science (New York, N.Y.). 290 [PubMed]
Vincent P et al. (2004). Out-of-sample extensions for LLE, Isomap, Mds, eigenmaps, and spectral clustering Advances in neural information processing systems. 16
Weiss Y. (1999). Segmentation using eigen vectors: A unifying view Proc IEEE Intl Conf Computer Vision.
Weiss Y, Jordan MI, Ng AY. (2002). On spectral clustering: Analysis and an algorithm Advances in neural information processing systems. 14
Williams C. (2001). On a connection between kernel pca and metric multidimensional scaling Advances in neural information processing systems. 13
Williams C, Seeger M. (2000). The effect of the input density distribution on kernel-based classifiers Proc 17th Intl Conf Mach Learn.
Williams CKI, Seeger M. (2001). Using the Nystrom method to speed up kernel machines Advances in neural information processing systems. 13
de Silva V, Tenenbaum J. (2003). Global versus local methods in nonlinear dimensionality reduction Advances in neural information processing systems. 15
Bengio Y, Monperrus M, Larochelle H. (2006). Nonlocal estimation of manifold structure. Neural computation. 18 [PubMed]