Learning the 2-D topology of images

Advances in Neural Information Processing Systems 20 - Proceedings of the 2007 Conference

Volumn , Issue , 2008, Pages

  Le Roux, Nicolas ^a   Bengio, Yoshua ^a   Lamblin, Pascal ^a   Joliveau, Marc ^b   Kégl, Balázs ^c  

^a UNIVERSITÉ DE MONTRÉAL   (Canada)

^b ECOLE CENTRALE PARIS   (France)

^c UNIVERSITÉ PARIS SUD   (France)

Author keywords

[No Author keywords available]

Indexed keywords

LEARNING ALGORITHMS; TOPOLOGY;

D-TOPOLOGY; DISTRIBUTIONAL SIMILARITIES; LEARNING TASKS; MANIFOLD LEARNING ALGORITHM; NATURAL IMAGES; PIXEL INTENSITIES; RELATIVE LOCATION; TWO-DIMENSIONAL; TWO-DIMENSIONAL STRUCTURES;

PIXELS;

EID: 85162039703     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (17)

1. S. Abdallah and M. Plumbley. Geometry dependency analysis. Technical Report C4DM-TR06-05, Center for Digital Music, Queen Mary, University of London, 2006.
2. Y. Bengio and Y. Le Cun. Scaling learning algorithms towards AI. In L. Bottou, O. Chapelle, D. DeCoste, and J. Weston, editors, Large Scale Kernel Machines. MIT Press, 2007.
3. G. Hinton, M. Welling, Y. Teh, and S. Osindero. A new view of ica. In Proceedings of ICA-2001, San Diego, CA, 2001.
4. A. Hyvärinen, P. O. Hoyer, and M. Inki. Topographic independent component analysis. Neural Computation, 13(7):1527-1558, 2001. (Pubitemid 33595031)
5. K. J. Lang and G. E. Hinton. The development of the time-delay neural network architecture for speech recognition. Technical Report CMU-CS-88-152, Carnegie-Mellon University, 1988.
6. Y. LeCun, B. Boser, J. Denker, D. Henderson, R. Howard, W. Hubbard, and L. Jackel. Backpropagation applied to handwritten zip code recognition. Neural Computation, 1(4):541-551, 1989.
7. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. Gradient based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278-2324, November 1998.
8. Y. LeCun and J. S. Denker. Natural versus universal probability complexity, and entropy. In IEEE Workshop on the Physics of Computation, pages 122-127. IEEE, 1992.
9. T.-W. Lee and M. S. Lewicki. Unsupervised classification segmentation and enhancement of images using ica mixture models. IEEE Trans. Image Proc., 11(3):270-279, 2002. (Pubitemid 34440331)
10. D. Lowe. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2):91-110, 2004.
11. S. Osindero, M. Welling, and G. Hinton. Topographic product models applied to natural scene statistics. Neural Computation, 18:381-344, 2005.
12. S. Roweis and L. Saul. Nonlinear dimensionality reduction by locally linear embedding. Science, 290(5500):2323-2326, Dec. 2000. (Pubitemid 32041578)
13. J. Tenenbaum, V. de Silva, and J. Langford. A global geometric framework for nonlinear dimensionality reduction. Science, 290(5500):2319-2323, Dec. 2000. (Pubitemid 32041577)
14. G. Tzanetakis and P. Cook. Musical genre classification of audio signals. IEEE Transactions on Speech and Audio Processing, 10(5):293-302, Jul 2002. (Pubitemid 34950067)
15. V. Vapnik. Estimation of Dependences Based on Empirical Data. Springer-Verlag, Berlin, 1982.
16. A. Waibel. Modular construction of time-delay neural networks for speech recognition. Neural Computation, 1:39-46, 1989.
17. K. Q. Weinberger and L. K. Saul. An introduction to nonlinear dimensionality reduction by maximum variance unfolding. In Proceedings of the National Conference on Artificial Intelligence (AAAI), Boston, MA, 2006.