Kernel information embeddings

ICML 2006 - Proceedings of the 23rd International Conference on Machine Learning

Volumn 2006, Issue , 2006, Pages 633-640

  Memisevic, Roland ^a  

^a UNIVERSITY OF TORONTO   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; DATA REDUCTION; FUNCTION EVALUATION; PARAMETER ESTIMATION;

CONDITIONAL DIMENSIONALITY REDUCTION; EMBEDDING ALGORITHMS; MUTUAL INFORMATION (MI);

INFORMATION ANALYSIS;

EID: 33749261821     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (18)

1. Bengio, Y., Paiement, J., Vincent, P., Delalleau, O., Roux, N. L., & Ouimet, M. (2003). Out-of-sample extensions for Ile, isomap, mds, eigenmaps, and spectral clustering. Adv. in Neural Information Processing Systems 16.
2. Cover, T., & Thomas, J. (1990). Elements of information theory. John Wiley and Sons.
3. Goldberger, J., Roweis, S., Hinton, G., & Salakhutdinov, R. (2005). Neighbourhood components analysis. Adv. in Neural Information Processing Systems 17.
4. Ham, J., Lee, D., & Saul, L. (2005). Semisupervised alignment of manifolds. Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics.
5. Hinton, G., & Roweis, S. (2003). Stochastic neighbor embedding. Adv. in Neural Information Processing Systems 15.
6. Kwok, J. T., & Tsang, I. W. (2003). The pre-image problem in kernel methods. Proc. of the Twentieth Intern. Conf. on Machine Learning.
7. Lawrence, N. D. (2004). Gaussian process latent variable models for visualisation of high dimensional data. Adv. in Neural Information Processing Systems 16.
8. Meinicke, P., Klanke, S., Memisevic, R., & Ritter, H. (2005). Principal surfaces from unsupervised kernel regression. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27, 1379-1391.
9. Memisevic, R., & Hinton, G. (2005). Multiple relational embedding. Adv. in Neural Information Processing Systems 17.
10. Nene, S. A., Nayar, S. K., & Murase, H. (1996). Columbia object image library (coil-20) (Technical Report).
11. Principe, J., Xu, D., & Fisher, J. (1999). Information theoretic learning. Unsupervised Adaptive Filtering (pp. 265-319). John Wiley & Sons.
12. Roweis, S., & Saul, L. K. (2000). Nonlinear dimensionality reduction by locally linear embedding. Science, 290, 2323-2326.
13. Sammon, J. W. (1969). A nonlinear mapping for data structure analysis. IEEE Transactions on Computers, 18, 401-409.
14. Schoelkopf, B., Smola, A., & Mueller, K.-R. (1998). Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, 10, 1299-1319.
15. Tenenbaum, J. B., & Freeman, W. T. (2000). Separating style and content with bilinear models. Neural Computation, 12, 1247-1283.
16. Tishby, N., Pereira, F., & Bialek, W. (1999). The information bottleneck method. Proceedings of the 37-th Annual Allerton Conference on Communication, Control and Computing.
17. Torkkola, K. (2003). Feature extraction by nonparametric mutual information maximization. Journal of Machine Learning Research, 3, 1415-1438.
18. Viola, P., Schraudolph, N. N., & Sejnowski, T. J. (1996). Empirical entropy manipulation for realworld problems. Adv. in Neural Information Processing Systems 8.