Connecting the Out-of-Sample and Pre-Image Problems in Kernel Methods
暂无分享,去创建一个
[1] A. Atiya,et al. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , 2005, IEEE Transactions on Neural Networks.
[2] Nicolas Le Roux,et al. Learning Eigenfunctions Links Spectral Embedding and Kernel PCA , 2004, Neural Computation.
[3] Ronen Basri,et al. Comparing images under variable illumination , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).
[4] Ivor W. Tsang,et al. The pre-image problem in kernel methods , 2003, IEEE Transactions on Neural Networks.
[5] Mikhail Belkin,et al. Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering , 2001, NIPS.
[6] D. Donoho,et al. Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data , 2003, Proceedings of the National Academy of Sciences of the United States of America.
[7] Trevor F. Cox,et al. Metric multidimensional scaling , 2000 .
[9] Steven W. Zucker,et al. Diffusion Maps and Geometric Harmonics for Automatic Target Recognition (ATR). Volume 2. Appendices , 2007 .
[10] Bernhard Schölkopf,et al. A kernel view of the dimensionality reduction of manifolds , 2004, ICML.
[11] Yogesh Rathi,et al. Shape-Based Approach to Robust Image Segmentation using Kernel PCA , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).
[12] Roman Goldenberg,et al. Behavior classification by eigendecomposition of periodic motions , 2005, Pattern Recognit..
[13] Gunnar Rätsch,et al. Kernel PCA and De-Noising in Feature Spaces , 1998, NIPS.
[14] Roman Goldenberg,et al. 'Dynamism of a Dog on a Leash' or Behavior Classification by Eigen-Decomposition of Periodic Motions , 2002, ECCV.
[15] J. Gower. Adding a point to vector diagrams in multivariate analysis , 1968 .
[16] Daniel Cremers,et al. Nonlinear Shape Statistics via Kernel Spaces , 2001, DAGM-Symposium.
[17] Kilian Q. Weinberger,et al. Learning a kernel matrix for nonlinear dimensionality reduction , 2004, ICML.
[18] David J. Fleet,et al. 3D People Tracking with Gaussian Process Dynamical Models , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).
[19] Ahmed M. Elgammal,et al. Gait tracking and recognition using person-dependent dynamic shape model , 2006, 7th International Conference on Automatic Face and Gesture Recognition (FGR06).
[20] Ronald R. Coifman,et al. Data Fusion and Multicue Data Matching by Diffusion Maps , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[21] J. Tenenbaum,et al. A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.
[22] Anuj Srivastava,et al. Cyclostationary Processes on Shape Spaces for Gait-Based Recognition , 2006, ECCV.
[23] Ron Kimmel,et al. Representation Analysis and Synthesis of Lip Images Using Dimensionality Reduction , 2006, International Journal of Computer Vision.
[24] Daniel Cremers,et al. Dynamical statistical shape priors for level set-based tracking , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[25] Bernhard Schölkopf,et al. Kernel Principal Component Analysis , 1997, ICANN.
[26] Guillermo Sapiro,et al. Dynamic Shapes Average , 2003 .
[27] Allen Tannenbaum,et al. Statistical shape analysis using kernel PCA , 2006, Electronic Imaging.
[28] Nicolas Le Roux,et al. Out-of-Sample Extensions for LLE, Isomap, MDS, Eigenmaps, and Spectral Clustering , 2003, NIPS.
[29] Namrata Vaswani,et al. IEEE TRANSACTIONS ON IMAGE PROCESSING 1 A Generic Framework for Tracking using Particle Filter with Dynamic Shape Prior , 2022 .
[30] S T Roweis,et al. Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.
[31] Bernhard Schölkopf,et al. Learning with kernels , 2001 .