Manifold clustering

Proceedings of the IEEE International Conference on Computer Vision

Volumn I, Issue , 2005, Pages 648-653

  Souvenir, Richard ^a   Piess, Robert ^a  

^a WASHINGTON UNIVERSITY IN ST LOUIS   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

MANIFOLD LEARNING; MOTION CAPTURE; MULTI-DIMENSIONAL SCALING; PARAMETERIZATION;

ALGORITHMS; DATA ACQUISITION; MATRIX ALGEBRA; NONLINEAR SYSTEMS; PARAMETER ESTIMATION; TOPOLOGY; VIDEO RECORDING;

LEARNING SYSTEMS;

EID: 33745937349     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2005.149     Document Type: Conference Paper

References (17)

1. M. Balasubramanian, E. L. Schwartz, J. B. Tenenbaum, V. de Silva, and J. C. Langford. The isomap algorithm and topological stability (technical comment). Science, 295(5552):7a-, 2002.
2. M. Belkin and P. Niyogi. Laplacian eigenmaps and spectral techniques for embedding and clustering. In T. G. Dietterich, S. Becker, and Z. Ghahramani, editors, Advances in Neural Information Processing Systems 14, pages 585-591, Cambridge, MA, 2002. MIT Press.
3. Y. Bengio and M. Monperrus. Non-local manifold tangent learning. In L. K. Saul, Y. Weiss, and L. Bottou, editors, Advances in Neural Information Processing Systems 17, pages 129-136. MIT Press, Cambridge, MA, 2005.
4. M. Brand. Charting a manifold. In S. T. S. Becker and K. Obermayer, editors, Advances in Neural Information Processing Systems 15, pages 961-968. MIT Press, Cambridge, MA, 2003.
5. J. D. Carroll and J.-J. Chang. Three-way scaling and clustering. Psychometrika, 35:238-319, 1970.
6. D. L. Donoho and C. Grimes. Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data. PNAS, 100(10):5591-5596, 2003.
7. A. Hadid and M. Pietikäinen. Efficient locally linear embeddings of imperfect manifolds. In P. Perner and A. Rosenfeld, editors, Machine Learning and Data Mining in Pattern Recognition, Third International Conference, MLDM 2003, pages 188-201, 2003.
8. A. Hyvarinen, J. Karhunen, and E. Oja. Independent Component Analysis. John Wiley and Sons, 2001.
9. I. T. Jolliffe. Principal Component Analysis. Springer-Verlag, 1986.
10. J. B. Kruskal and M. Wish. Multidimensional scaling. Sage University Paper series on Quantitative Application in the Social Sciences, pages 07-011, 1978.
11. E. Levina and P. J. Bickel. Maximum likelihood estimation of intrinsic dimension. In L. K. Saul, Y. Weiss, and L. Bottou, editors, Advances in Neural Information Processing Systems 17, pages 777-784. MIT Press, Cambridge, MA, 2005.
12. R. Pless and I. Simon. Embedding images in non-flat spaces. In Proc. of the International Conference on Imaging Science, Systems, and Technology, 2002.
13. S. Roweis and L. K. Saul. Nonlinear dimensionality reduction by locally linear embedding. Science, 290:2323-2326, December 2000.
14. N. Srebro and T. Jaakkola. Weighted low-rank approximations. In Proceedings of the Twentieth International Conference on Machine Learning (ICML-2003), 2003.
15. J. Tenebaum, V. de Silva, and J. Langford. A global geometric framework for nonlinear dimensionality reduction. Science, 290:2319-2323, December 2000.
16. K. Q. Weinberger and L. K. Saul. Unsupervised learning of image manifolds by semidefinite programming. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR-04), volume II, pages 988-995, Washington D.C., 2004.
17. Y. Wu and K. L. Chan. An extended isomap algorithm for learning multi-class manifold. In Proceedings of IEEE International Conference on Machine Learning and Cybernetics (ICMLC2004), Shanghai, China, 2004.