Twin Kernel Embedding with back constraints

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2007, Pages 319-324

  Guo, Yi ^a   Kwan, Paul W ^a   Gao, Junbin ^b  

^a UNIVERSITY OF NEW ENGLAND   (Australia)

^b CHARLES STURT UNIVERSITY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

ADMINISTRATIVE DATA PROCESSING; CANNING; DATA MINING; DECISION SUPPORT SYSTEMS; INFORMATION MANAGEMENT; KINETIC ENERGY; MINING; SEARCH ENGINES;

EMBEDDINGS; EXPERIMENTAL EVALUATIONS; GRAM MATRICES; HIGH-DIMENSIONAL; INPUT DATA; INTERNATIONAL CONFERENCES; LOW DIMENSIONAL; REAL-WORLD DATA SETS; SIMILARITY RELATIONS;

CONFORMAL MAPPING;

EID: 46149095008     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDMW.2007.15     Document Type: Conference Paper

References (12)

1. M. Belkin and P. Niyogi. Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation, 15(6):1373-1396, 2003.
2. Y. Bengio, J. Paiement, P. Vincent, O. Delalleau, N. L. Roux, and M. Ouimet. Out-of-sample extensions for lle, isomap, mds, eigenmaps, and spectral clustering. In Advances in Neural Information Processing Systems 16.
3. M. Brand. Charting a manifold. In Advances in Neural Information Processing Systems 15. MIT Press.
4. Y. Guo and J. Gao. An integration of shape context and semigroup kernel in image classification. In International Conference on Machine Learning and Cybernetics, 2007.
5. Y. Guo, J. Gao, and P. W. Kwan. Kernel Laplacian eigen-maps for visualization of non-vectorial data. In A. Sattar and B. Kang, editors, Lecture Notes on Artificial Intelligent, volume 4304, pages 1179-1183, Berlin Heidelberg, December 2006. Springer-Verlag.
6. Y. Guo, J. Gao, and P. W. Kwan. Visualization of non-vectorial data using twin kernel embedding. AIDM, 0:11-17, 2006.
7. X. He, D. Cai, S. Yan, and H.-J. Zhang. Neighborhood preserving embedding. ICCV, 2:1208-1213, 2005.
8. X. He and P. Niyogi. Locality preserving projections. In S. Thrun, L. Saul, and B. Schölkopf, editors, Advances in Neural Information Processing Systems 16. MIT Press, Cambridge, MA, 2004.
9. N. D. Lawrence and J. Quiñonero-Candela. Local distance preservation in the GP-LVM through back constraints. In W. Cohen and A. Moore, editors, Proceedings of the International Conference in Machine Learning, San Francisco, CA, 2006. Morgan Kauffman.
10. H. Lodhi, C. Saunders, J. Shawe-Taylor, N. Cristianini, and C. J. C. H. Watkins. Text classification using string kernels. Journal of Machine Learning Research, 2:419-444, 2002.
11. C. E. Rasmussen and C. K. I. Williams. Gaussian Processes for Machine Learning. Adaptive computation and machine learning. The MIT Press, Cambridge, Massachusetts, 2006.
12. G. Salton, A. Wong, and C. S. Yang. A vector space model for automatic indexing. Information Retieval and Language Processing, 18(11):613-620, November 1975.