Riemannian manifold learning for nonlinear dimensionality reduction

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3951 LNCS, Issue , 2006, Pages 44-55

  Lin, Tony ^a   Zha, Hongbin ^a   Lee, Sang Uk ^b  

^a PEKING UNIVERSITY   (China)

^b Seoul National University   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTATIONAL COMPLEXITY; PATTERN RECOGNITION; VISION;

GEOMETRIC STRUCTURE; RIEMANNIAN MANIFOLD LEARNING (RML); RIEMANNIAN NORMAL COORDINATES (RNC);

LEARNING SYSTEMS;

EID: 33745854283     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11744023_4     Document Type: Conference Paper

References (8)

1. Donoho, D.: High-Dimensional Data Analysis: The Curses and Blessings of Dimensionality. Mathematical Challenges of the 21st Century. American Mathematical Society, Los Angeles, CA (2000)
2. Jolliffe, I.: Principal Component Analysis. Springer-Verlag, New York (1989)
3. Cox, T., Cox, M.: Multidimensional Scaling. Chapman & Hall, London (1994)
4. Bourlard, H., Kamp, Y.: Auto-association by multilayer perceptrons and singular value decomposition. Biological Cybernetics 59 (1988) 291-294
5. Erwin, E., Obermayer, K., Schulten, K.: Self-organizing maps: Ordering, convergence properties and energy functions. Biological Cybernetics 67 (1992) 47-55
6. Durbin, R., Willshaw, D.: An analogue approach to the travelling salesman problem using an elastic net method. Nature 326 (1987) 689-691
7. Bishop, C., Svenson, M., Williams, C.: Gtm: The generative topographic mapping. Neural Computation 10 (1998) 215-234
8. Hastie, T., Stuetzle, W.: Principal curves. J. American Statistical Association 84 (1989) 502-516