From graph to manifold Laplacian: The convergence rate

Applied and Computational Harmonic Analysis

Volumn 21, Issue 1, 2006, Pages 128-134

  Singer, A ^a  

^a YALE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 33745397832     PISSN: 10635203     EISSN: 1096603X     Source Type: Journal    
DOI: 10.1016/j.acha.2006.03.004     Document Type: Letter

References (11)

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2. M. Belkin, Problems of learning on manifolds, Ph.D. dissertation, University of Chicago, 2003
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4. Belkin M., and Niyogi P. Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput. 15 6 (2003) 1373-1396
5. Belkin M., and Niyogi P. Towards a theoretical foundation for Laplacian-based manifold methods. In: Auer P., and Meir R. (Eds). Proc. 18th Conf. Learning Theory (COLT), Lecture Notes Comput. Sci. vol. 3559 (2005), Springer-Verlag, Berlin 486-500
6. S. Lafon, Diffusion maps and geometric harmonics, Ph.D. dissertation, Yale University, 2004
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8. Coifman R.R., Lafon S., Lee A.B., Maggioni M., Nadler B., Warner F., and Zucker S.W. Geometric diffusions as a tool for harmonic analysis and structure definition of data: Diffusion maps. Proc. Natl. Acad. Sci. 102 21 (2005) 7426-7431
9. Coifman R.R., Lafon S., Lee A.B., Maggioni M., Nadler B., Warner F., and Zucker S.W. Geometric diffusions as a tool for harmonic analysis and structure definition of data: Multiscale methods. Proc. Natl. Acad. Sci. 102 21 (2005) 7432-7437
10. Smolyanov O.G., Weizsäcker H.V., and Wittich O. Brownian motion on a manifold as limit of stepwise conditioned standard Brownian motions. In: Gesztesy F. (Ed). Stochastic Processes, Physics and Geometry, New Interplays, II, CMS Conf. Proc. vol. 29 (2000), Amer. Math. Soc., Providence, RI
11. d. In: De Raedt L., and Wrobel S. (Eds). Proc. 22nd Int. Conf. Machine Learning (2005), ACM 289-296