A Riemannian approach to graph embedding
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[1] Michael Brady,et al. Feature-based correspondence: an eigenvector approach , 1992, Image Vis. Comput..
[2] J. Jost. Riemannian geometry and geometric analysis , 1995 .
[3] S. Zucker. Estimates for the classical parametrix for the Laplacian , 1978 .
[4] Ulrike von Luxburg,et al. Limits of Spectral Clustering , 2004, NIPS.
[5] Herbert Busemann,et al. The geometry of geodesics , 1955 .
[6] Kim L. Boyer,et al. Quantitative measures of change based on feature organization: eigenvalues and eigenvectors , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[7] H. Piaggio. Differential Geometry of Curves and Surfaces , 1952, Nature.
[8] William J. Christmas,et al. Structural Matching in Computer Vision Using Probabilistic Relaxation , 1995, IEEE Trans. Pattern Anal. Mach. Intell..
[9] Jitendra Malik,et al. Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[10] W. Torgerson. Multidimensional scaling: I. Theory and method , 1952 .
[11] Christopher G. Harris,et al. A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.
[12] Edwin R. Hancock,et al. Iterative Procrustes alignment with the EM algorithm , 2002, Image Vis. Comput..
[13] J. Mccleary,et al. Geometry from a Differentiable Viewpoint: Recapitulation and coda , 1994 .
[14] Philip H. S. Torr,et al. The Development and Comparison of Robust Methods for Estimating the Fundamental Matrix , 1997, International Journal of Computer Vision.
[15] Timothy F. Cootes,et al. Active Shape Models-Their Training and Application , 1995, Comput. Vis. Image Underst..
[16] Mikhail Belkin,et al. Towards a theoretical foundation for Laplacian-based manifold methods , 2005, J. Comput. Syst. Sci..
[17] Josef Kittler,et al. Discrete relaxation , 1990, Pattern Recognit..
[18] I. Chavel. Riemannian Geometry: Subject Index , 2006 .
[19] H. C. Longuet-Higgins,et al. An algorithm for associating the features of two images , 1991, Proceedings of the Royal Society of London. Series B: Biological Sciences.
[20] A. Singer. From graph to manifold Laplacian: The convergence rate , 2006 .
[21] Patrick J. F. Groenen,et al. Modern Multidimensional Scaling: Theory and Applications , 2003 .
[22] Terry Caelli,et al. An eigenspace projection clustering method for inexact graph matching , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[23] S. Ullman,et al. Filling-in the gaps: The shape of subjective contours and a model for their generation , 1976, Biological Cybernetics.
[24] Andrew Ranicki,et al. Algebraic L-theory and Topological Manifolds , 1993 .
[25] Huiling Le,et al. Multidimensional scaling of simplex shapes , 1999, Pattern Recognit..
[26] I. Chavel. Eigenvalues in Riemannian geometry , 1984 .
[27] P. Groenen,et al. Modern multidimensional scaling , 1996 .
[28] Ulrike von Luxburg,et al. From Graphs to Manifolds - Weak and Strong Pointwise Consistency of Graph Laplacians , 2005, COLT.
[29] Mikhail Belkin,et al. Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering , 2001, NIPS.
[30] Kim L. Boyer,et al. Quantitative Measures of Change Based on Feature Organization: Eigenvalues and Eigenvectors , 1998, Comput. Vis. Image Underst..
[31] W. Torgerson,et al. Multidimensional scaling of similarity , 1965, Psychometrika.
[32] S. Rosenberg. The Laplacian on a Riemannian Manifold: The Laplacian on a Riemannian Manifold , 1997 .
[33] H. Flanders. Differential Forms with Applications to the Physical Sciences , 1964 .
[34] Ioannis G. Tollis,et al. Efficient Orthogonal Drawings of High Degree Graphs , 2000, Algorithmica.
[35] T. J. Willmore,et al. DIFFERENTIAL FORMS WITH APPLICATIONS TO THE PHYSICAL SCIENCES , 1967 .
[36] Steven Gold,et al. A Graduated Assignment Algorithm for Graph Matching , 1996, IEEE Trans. Pattern Anal. Mach. Intell..
[37] Fan Chung,et al. Spectral Graph Theory , 1996 .
[38] Alla Sheffer,et al. Fundamentals of spherical parameterization for 3D meshes , 2003, ACM Trans. Graph..
[39] B. O'neill. Elementary Differential Geometry , 1966 .
[40] J. Tenenbaum,et al. A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.
[41] King-Sun Fu,et al. IEEE Transactions on Pattern Analysis and Machine Intelligence Publication Information , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[42] Roberto Tamassia. On-Line Planar Graph Embedding , 1996, J. Algorithms.
[43] Edwin R. Hancock,et al. Structural Graph Matching Using the EM Algorithm and Singular Value Decomposition , 2001, IEEE Trans. Pattern Anal. Mach. Intell..
[44] Hanan Samet,et al. Properties of Embedding Methods for Similarity Searching in Metric Spaces , 2003, IEEE Trans. Pattern Anal. Mach. Intell..
[45] John D. Lafferty,et al. Diffusion Kernels on Statistical Manifolds , 2005, J. Mach. Learn. Res..
[46] N. Hitchin. A panoramic view of riemannian geometry , 2006 .
[47] A. Householder,et al. Discussion of a set of points in terms of their mutual distances , 1938 .
[48] Shinji Umeyama,et al. An Eigendecomposition Approach to Weighted Graph Matching Problems , 1988, IEEE Trans. Pattern Anal. Mach. Intell..
[49] S T Roweis,et al. Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.