Manifold learning using euclidean k-nearest neighbor graphs

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

Volumn 3, Issue , 2004, Pages

  Costa, Jose A ^a   Hero III, Alfred O ^a  

^a UNIVERSITY OF MICHIGAN   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EUCLIDEAN FUNCTIONALS; GEODESIC MINIMAL SPANNING TREE (GMST); INTRINSIC ENTROPY; MANIFOLD LEARNING;

ASYMPTOTIC STABILITY; EMBEDDED SYSTEMS; GRAPH THEORY; IMAGING TECHNIQUES; INTEGER PROGRAMMING; LEAST SQUARES APPROXIMATIONS; METHOD OF MOMENTS; PARAMETER ESTIMATION; SET THEORY; SIGNAL PROCESSING; VECTORS;

LEARNING SYSTEMS;

EID: 4544326792     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (11)

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6. J. A. Costa and A. O. Hero III, "Geodesic entropic graphs for dimension and entropy estimation in manifold learning," IEEE Trans. on Signal Processing, to appear, 2004.
7. J. E. Yukich, Probability theory of classical Euclidean optimization problems, vol. 1675 of Lecture Notes in Mathematics, Springer-Verlag, Berlin, 1998.
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9. J. A. Costa and A. O. Hero III, "Manifold learning using Euclidean k-nearest neighbor graphs," in preparation, 2003.
10. V. de Silva and J. B. Tenenbaum, "Global versus local methods in nonlinear dimensionality reduction," in Neural Information Processing Systems 15 (NIPS), Vancouver, Canada, Dec. 2002.
11. A. Georghiades, P. Belhumeur, and D. Kriegman, "From few to many: Illumination cone models for face recognition under variable lighting and pose," IEEE Trans. Pattern Anal. Mach. Intelligence, vol. 23, no. 6, pp. 643-660, 2001.