A robust generalization of ISOMAP for new data

2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005

Volumn , Issue , 2005, Pages 1707-1712

  Shi, Lu Kui ^a,b   He, Pi Lian ^a   Liu, Bin ^c   Fu, Kun ^b   Wu, Qing ^b  

^a TIANJIN UNIVERSITY   (China)

^b HEBEI UNIVERSITY OF TECHNOLOGY   (China)

^c China Securities   (China)

Author keywords

Geodesic distances; ISOMAP; Manifold; Nonlinear dimensionality reduction

Indexed keywords

ALGORITHMS; DATA ACQUISITION; EIGENVALUES AND EIGENFUNCTIONS; EMBEDDED SYSTEMS; MATHEMATICAL MODELS;

ISOMAP; MANIFOLD; NONLINEAR DIMENSIONALITY REDUCTION;

NONLINEAR SYSTEMS;

EID: 28444454040     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (10)

1. I.T. Jolliffe, Principal component analysis, Springer-Verlag, New York, 1989.
2. T.F. Cox and M.A.A. Cox, Multidimensional scaling, Chapman and Hall, London, 1994.
3. J. B. Tenenbaum, V. de Silva, and J. C. Langford, A global geometric framework for nonlinear dimensionality reduction, Science, Vol. 290, pp. 2319-2323, December 2000.
4. V. De Silva and J. B. Tenenbaum, Global versus local methods in nonlinear dimensionality reduction, In Advances in Neural Information Processing Systems, Vol. 15, pp. 705-712, 2002.
5. S. T. Rowei and L. K. Saul, Nonlinear dimensionality reduction by locally linear embedding, Science, Vol. 290, pp. 2323-2326, December 2000.
6. M. Belkin and P. Niyogi, Laplacian eigenmaps for dimensionality reduction and data representation, Neural Computation, Vol.15, pp. 1373-1396, 2003.
7. J. Ham, D. D. Lee, S. Mika, and B. Schölkopf, A kernel view of the dimensionality reduction of manifolds, Proceedings of the Twenty First International Conference on Machine Learning (ICML-04), Banff, Canada, pp.369-376, 2004.
8. D. K. Agrafiotis and Huafeng Xu, A self-organizing principle for learning nonlinear manifolds, PNAS, pp. 15869-15872, 2002.
9. Martin H. C. Law, Nan Zhang, and Anil K. Jain, Nonlinear manifold learning for data stream, Proceedings of SIAM Data Mining, Florida, USA, pp.33-44, 2004.
10. Yuexian Hou, Zheng Ding, and Pilian He, Self-organizing isometric embedding, Chinese Journal of Computer Research and Development, Vol. 42, No. 2, pp. 188-195, 2005.