Belkin, M. and Niyogi, P. 2003. Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation
, 15(6):1373–1396.CrossRefMATHGoogle Scholar
Belongie, S., Malik, J., and Puzicha, J. 2002. Shape matching and object recognition using shape contexts. IEEE Transactions on Pattern Analysis and Machine Intelligence
, 24(4):509–522.CrossRefGoogle Scholar
Bengio, Y., Paiement, J.F., and Vincent, P. 2004. Out-of-sample extensions for LLE, Isomap, MDS, eigenmaps, and spectral clustering. Advances in Neural Information Processing Systems 16. Cambridge, MA: MIT Press.
Beymer, D. and Poggio, T. 1996. Image representation for visual learning. Science, 272, 1905.
Biswas, P. and Ye, Y. 2003. A distributed method for solving semidefinite programs arising from ad hoc wireless sensor network localization. Stanford University, Department of Electrical Engineering, working paper.
Blitzer, J., Weinberger, K.Q., Saul, L.K., and Pereira, F.C.N. 2005. Hierarchical distributed representations for statistical language modeling. Advances in Neural and Information Processing Systems. Cambridge, MA: MIT Press.
Borchers, B. 1999. CSDP, a C library for semidefinite programming. Optimization Methods and Software
, 11(1):613–623.MATHMathSciNetGoogle Scholar
Bowling, M., Ghodsi, A., and Wilkinson, D. 2005. Action respecting embedding. In Proceedings of the Twenty-second International Conference on Machine Learning (ICML 2005). Bonn, Germany.
Brand, M. 2003. Charting a manifold. Advances in Neural Information Processing Systems 15
, Cambridge, MA: MIT Press, pp. 985–992.Google Scholar
Burer, S. and Monteiro, R. 2003. A nonlinear programming algorithm for solving semidefinite programs via low-rank factorization. Mathematical Programming, Series, B95, pp. 329–357.
Burges, C.J.C. 2005. Geometric methods for feature extraction and dimensional reduction. In L. Rokach and O. Maimon (eds.), Data Mining and Knowledge Discovery Handbook: A Complete Guide for Practitioners and Researchers. Kluwer Academic Publishers.
Cox, T. and Cox, M. 1994. Multidimensional Scaling. London: Chapman and Hall.
Donoho, D.L. and Grimes, C.E. 2002. When Does Isomap Recover The Natural Parameterization of Families of Articulated Images? (Technical Report 2002–27). Department of Statistics. Stanford University.
Donoho, D.L. and Grimes, C.E. 2003. Hessian eigenmaps: locally linear embedding techniques for high-dimensional data. In Proceedings of the National Academy of Arts and Sciences 100, pp. 5591–5596.
Elgammal, A. and Lee, C. 2004. Separating style and content on a nonlinear manifold. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2004), Washington DC, pp. 478–485.
Fazel, M., Hindi, H., and Boyd, S.P. 2001. A rank minimization heuristic with application to minimum order system approximation. In Proceedings of the American Control Conference, pp. 4734–4739.
Gordon, S., Goldberger, J., and Greenspan, H. 2003. Applying the information bottleneck principle to unsupervised clustering of discrete and continuous image representations. In Proceedings of the Ninth International Conference on Computer Vision (ICCV 2003), pp. 370–377.
Ham, J., Lee, D.D., Mika, S., and Schölkopf, B. 2003. A kernel view of the dimensionality reduction of manifolds (Technical Report TR-110). Max-Planck-Institut für Biologische Kybernetik, Tübingen.
Hull, J.J. 1994. A database for handwritten text recognition research. IEEE Transaction on Pattern Analysis and Machine Intelligence
Jolliffe, I.T. 1986. Principal Component Analysis. New York: Springer-Verlag.
Lanckriet, G.R.G., Christianini, N., Bartlett, P.L., Ghaoui, L.E., and Jordan, M.I. 2002. Learning the kernel matrix with semi-definite programming. In Proceedings of the Nineteenth International Conference on Machine Learning (ICML 2002), pp. 323–330.
Lee, D.D., and Seung, H.S. 1999. Learning the parts of objects with nonnegative matrix factorization. Nature
, 401:788–791.CrossRefGoogle Scholar
Lee, K., Ho, J., Yang, M.-H., and Kriegman, D. 2003. Video-based face recognition using probabilistic appearance manifolds. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2003), pp. 313–320.
Lu, H., Fainman, Y., and Hecht-Nielsen, R. 1998. Image manifolds. Applications of Artificial Neural Networks in Image Processing III, Proceedings of SPIE, Bellingham, WA: SPIE, pp. 52–63.
Nash, J. 1956. The imbedding problem for riemannian manifolds. Annals of Mathematics pp. 20–63.
Ng, A.Y., Jordan, M., and Weiss, Y. 2002. On spectral clustering: analysis and an algorithm. Advances in Neural Information Processing Systems14 Cambridge, MA: MIT Press, pp. 849–856.
Pless, R. 2004. Differential structure in non-linear image embedding functions. Proceedings of the IEEE Workshop on Articulated and Non-Rigid Motion, Washington, D.C. pp. 10–17.
Pless, R. and Simon, I. 2002. Embedding images in non-flat spaces. In Proceedings of the Conference on Imaging Science Systems and Technology pp. 182–188.
Roweis, S.T. and Saul, L.K. 2000). Nonlinear dimensionality reduction by locally linear embedding. Science
, 290:2323–2326.Google Scholar
Saul, L.K. and Roweis, S.T. 2003. Think globally, fit locally: unsupervised learning of low dimensional manifolds. Journal of Machine Learning Research
, 4:119–155.MathSciNetCrossRefGoogle Scholar
Schölkopf, B. and Smola, A.J. 2002. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Cambridge, MA: MIT Press.
Schölkopf, B., Smola, A.J., and Müller, K.R. 1998. Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation
, 10:1299–1319.CrossRefGoogle Scholar
Seung, H.S. and Lee, D.D. 2000. The manifold ways of perception. Science
, 290:2268–2269.CrossRefGoogle Scholar
Sha, F. and Saul, L.K. 2005. Analysis and extension of spectral methods for nonlinear dimensionality reduction. In Proceedings of the Twenty-second International Conference on Machine Learning (ICML 2005). Bonn, Germany.
Shi, J. and Malik, J. 2000. Normalized cuts and image segmentation. In IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), pp. 888–905.
Simard, P.Y., LeCun, Y., and Decker, J. 1993. Efficient pattern recognition using a new transformation distance. Advances in Neural Information Processing Systems, San Mateo, CA: Morgan Kaufman, pp. 50–58.
Souvenir, R. and Pless, R. 2005. Isomap and non-parametric models of image deformation. In Proceedings of the IEEE Workshop on Motion and Video Computing, Breckenridge, CO., pp. 195–200.
Sun, J., Boyd, S., Xiao, L., and Diaconis, P. 2004. The fastest mixing Markov process on a graph and a connection to a maximum variance unfolding problem. Submitted to SIAM Review.
Tenenbaum, J.B., de Silva, V., and Langford, J.C. 2000. A global geometric framework for nonlinear dimensionality reduction. Science
, 290:2319–2323.CrossRefGoogle Scholar
Turk, M. and Pentland, A. 1991. Eigenfaces for recognition. Journal of Cognitive Neuroscience
, 3(1):71–86.CrossRefGoogle Scholar
Vandenberghe, L. and Boyd, S.P. 1996. Semidefinite programming. SIAM Review
, 38(1):49–95.MathSciNetCrossRefMATHGoogle Scholar
Vapnik, V. 1998. Statistical Learning Theory. N.Y.: Wiley.
Weinberger, K.Q., Packer, B.D., and Saul, L.K. 2005. Nonlinear dimensionality reduction by semidefinite programming and kernel matrix factorization. In Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics. Barbados, West Indies.
Weinberger, K.Q. and Saul, L.K. 2004. Unsupervised learning of image manifolds by semidefinite programming. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR-04), Washington D.C, pp. 988–995.
Weinberger, K.Q., Sha, F., and Saul, L.K. 2004. Learning a kernel matrix for nonlinear dimensionality reduction. In Proceedings of the Twenty First International Conference on Machine Learning (ICML-04), Banff, Canada, pp. 839–846.
Zha, H. and Zhang, Z. 2003. Isometric embedding and continuum Isomap. In Proceedings of the Twentieth International Conference on Machine Learning (ICML 2003), pp. 864–871.
Zhang, Z. and Zha, H. 2004. Principal manifolds and nonlinear dimensionality reduction by local tangent space alignment. SIAM Journal of Scientific Computing
, 26:313–338.MathSciNetCrossRefMATHGoogle Scholar