The manifold tangent classifier

Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011

Volumn , Issue , 2011, Pages

  Rifai, Salah ^a   Dauphin, Yann N ^a   Vincent, Pascal ^a   Bengio, Yoshua ^a   Muller, Xavier ^a  

^a UNIVERSITÉ DE MONTRÉAL   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

DOMAIN KNOWLEDGE;

DATA DENSITY; DIFFERENT CLASS; HIGH-ORDER; HIGHER-ORDER; INPUT DISTRIBUTIONS; LOWER DENSITY; LOWER DIMENSIONAL MANIFOLDS; MANIFOLD STRUCTURES; NOVEL ALGORITHM; SEMI-SUPERVISED;

CLASSIFICATION (OF INFORMATION);

EID: 85162427692     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (30)

1. Bengio, Y. (2009). Learning deep architectures for AI. Foundations and Trends in Machine Learning, 2(1), 1-127. Also published as a book. Now Publishers, 2009.
2. Bengio, Y. and Monperrus, M. (2005). Non-local manifold tangent learning. In NIPS'04, pages 129-136. MIT Press.
3. Bengio, Y., Larochelle, H., and Vincent, P. (2006). Non-local manifold parzen windows. In NIPS'05, pages 115-122. MIT Press.
4. Bengio, Y., Lamblin, P., Popovici, D., and Larochelle, H. (2007). Greedy layer-wise training of deep networks. In Advances in NIPS 19.
5. Brand, M. (2003). Charting a manifold. In NIPS'02, pages 961-968. MIT Press.
6. Cayton, L. (2005). Algorithms for manifold learning. Technical Report CS2008-0923, UCSD.
7. Drucker, H. and LeCun, Y. (1992). Improving generalisation performance using double back-propagation. IEEE Transactions on Neural Networks, 3(6), 991-997.
8. Erhan, D., Bengio, Y., Courville, A., Manzagol, P.-A., Vincent, P., and Bengio, S. (2010). Why does unsupervised pre-training help deep learning? JMLR, 11, 625-660.
9. Goodfellow, I., Le, Q., Saxe, A., and Ng, A. (2009). Measuring invariances in deep networks. In NIPS'09, pages 646-654.
10. Hinton, G. E., Osindero, S., and Teh, Y. (2006). A fast learning algorithm for deep belief nets. Neural Computation, 18, 1527-1554.
11. Kavukcuoglu, K., Ranzato, M., Fergus, R., and LeCun, Y. (2009). Learning invariant features through topographic filter maps. pages 1605-1612. IEEE.
12. Lasserre, J. A., Bishop, C. M., and Minka, T. P. (2006). Principled hybrids of generative and discriminative models. pages 87-94, Washington, DC, USA. IEEE Computer Society.
13. LeCun, Y., Haffner, P., Bottou, L., and Bengio, Y. (1999). Object recognition with gradient-based learning. In Shape, Contour and Grouping in Computer Vision, pages 319-345. Springer.
14. Narayanan, H. and Mitter, S. (2010). Sample complexity of testing the manifold hypothesis. In J. Lafferty, C. K. I. Williams, J. Shawe-Taylor, R. Zemel, and A. Culotta, editors, Advances in Neural Information Processing Systems 23, pages 1786-1794.
15. Ranzato, M., Poultney, C., Chopra, S., and LeCun, Y. (2007a). Efficient learning of sparse representations with an energy-based model. In NIPS'06.
16. Ranzato, M., Huang, F., Boureau, Y., and LeCun, Y. (2007b). Unsupervised learning of invariant feature hierarchies with applications to object recognition. IEEE Press.
17. Rifai, S., Vincent, P., Muller, X., Glorot, X., and Bengio, Y. (2011a). Contracting auto-encoders: Explicit invariance during feature extraction. In Proceedings of the Twenty-eight International Conference on Machine Learning (ICML'11).
18. Rifai, S., Mesnil, G., Vincent, P., Muller, X., Bengio, Y., Dauphin, Y., and Glorot, X. (2011b). Higher order contractive auto-encoder. In European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD).
19. Roweis, S. and Saul, L. K. (2000). Nonlinear dimensionality reduction by locally linear embedding. Science, 290(5500), 2323-2326.
20. Salakhutdinov, R. and Hinton, G. E. (2007). Learning a nonlinear embedding by preserving class neighbourhood structure. In AISTATS'2007, San Juan, Porto Rico. Omnipress.
21. Salakhutdinov, R. and Hinton, G. E. (2009). Deep Boltzmann machines. In AISTATS'2009, volume 5, pages 448-455.
22. Simard, P., Victorri, B., LeCun, Y., and Denker, J. (1992). Tangent prop - A formalism for specifying selected invariances in an adaptive network. In NIPS'91, pages 895-903, San Mateo, CA. Morgan Kaufmann.
23. Simard, P. Y., LeCun, Y., and Denker, J. (1993). Efficient pattern recognition using a new transformation distance. In NIPS'92, pages 50-58. Morgan Kaufmann, San Mateo.
24. Tenenbaum, J., de Silva, V., and Langford, J. C. (2000). A global geometric framework for nonlinear dimensionality reduction. Science, 290(5500), 2319-2323.
25. Trebar, M. and Steele, N. (2008). Application of distributed svm architectures in classifying forest data cover types. Computers and Electronics in Agriculture, 63(2), 119-130.
26. Vincent, P. and Bengio, Y. (2003). Manifold parzen windows. In NIPS'02. MIT Press.
27. Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., and Manzagol, P.-A. (2010). Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. JMLR, 11(3371-3408).
28. Weston, J., Ratle, F., and Collobert, R. (2008). Deep learning via semi-supervised embedding. In ICML 2008, pages 1168-1175, New York, NY, USA.
29. Yu, K. and Zhang, T. (2010). Improved local coordinate coding using local tangents.
30. Yu, K., Zhang, T., and Gong, Y. (2009). Nonlinear learning using local coordinate coding. In Y. Bengio, D. Schuurmans, J. Lafferty, C. K. I. Williams, and A. Culotta, editors, Advances in Neural Information Processing Systems 22, pages 2223-2231.