Gaussian process latent random field

Proceedings of the National Conference on Artificial Intelligence

Volumn 1, Issue , 2010, Pages 679-684

  Zhong, Guoqiang ^a   Li, Wu Jun ^b   Yeung, Dit Yan ^b   Hou, Xinwen ^a   Liu, Cheng Lin ^a  

^a INSTITUTE OF AUTOMATION   (China)

^b HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Hong Kong)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; CLASSIFICATION (OF INFORMATION); EQUIVALENCE CLASSES; GAUSSIAN NOISE (ELECTRONIC); MARKOV PROCESSES; PRINCIPAL COMPONENT ANALYSIS; RANDOM VARIABLES;

CLASSIFICATION PERFORMANCE; GAUSSIAN MARKOV RANDOM FIELD; GAUSSIAN PROCESSES; INTRINSIC DIMENSIONALITIES; LATENT VARIABLE MODELING; NONLINEAR DIMENSIONALITY REDUCTION; PROBABILISTIC MODELING; STATE-OF-THE-ART METHODS;

GAUSSIAN DISTRIBUTION;

EID: 77958606852     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (19)

1. Baudat, G., and Anouar, F. 2000. Generalized discriminant analysis using a kernel approach. Neural Computation 12:2385-2404.
2. Belkin, M., and Niyogi, P. 2001. Laplacian eigenmaps and spectral techniques for embedding and clustering. In Advances in Neural Information Processing Systems, 585-591.
3. Chung, F. 1997. Spectral Graph Theory. Number 92 in Regional Conference Series in Mathematics. American Mathematical Society.
4. Cox, T., and Cox, M. 2001. Multidimensional Scaling. Chapman and Hall, Boca Raton.
5. Fukunnaga, K. 1991. Introduction to Statistical Pattern Recognition, second edition. Academic Press.
6. Geiger, A.; Urtasun, R.; and Darrell, T. 2009. Rank priors for continuous nonlinear dimensionality reduction. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 880-887.
7. He, X., and Niyogi, P. 2003. Locality preserving projections. In Advances in Neural Information Processing Systems.
8. Joliffe, I. 1986. Principal Component Analysis. Springer-Verlag.
9. Lawrence, N., and Quinonero-Candela, J. 2006. Local distance preservation in the GP-LVM through back constraints. In Proceedings of the International Conference on Machine Learning, 96-103.
10. Lawrence, N. 2005. Probabilistic non-linear principal component analysis with Gaussian process latent variable models. Journal of Machine Learning Research 6:1783-1816.
11. Møler, F. 1993. A scaled conjugate gradient algorithm for fast supervised learning. Neural Networks 6(4):525-533.
12. Rasmussen, C. E., and Williams, C. 2006. Gaussian Processes for Machine Learning. Monographs on Statistics and Applied Probability. MIT Press.
13. Roweis, S., and Saul, L. 2000. Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500):2323-2326.
14. Rue, H., and Held, L. 2005. Gaussian Markov Random Fields: Theory and Applications, volume 104 of Monographs on Statistics and Applied Probability. London: Chapman & Hall.
15. Tenenbaum, J.; Silva, V.; and Langford, J. 2000. A global geometric framework for nonlinear dimensionality reduction. Science 290(5500):2319-2323.
16. Urtasun, R., and Darrell, T. 2007. Discriminative Gaussian process latent variable models for classification. In Proceedings of the International Conference on Machine Learning, 927-934. (Pubitemid 350094070)
17. Urtasun, R.; Fleet, D. J.; Geiger, A.; Popovic, J.; Darrell, T; and Lawrence, N. 2008. Topologically-constrained latent variable models. In Proceedings of the International Conference on Machine Learning, 1080-1087.
18. van der Maaten, L. 2009. Preserving local structure in Gaussian process latent variable models. In Proceedings of the 18th Annual Belgian-Dutch Conference on Machine Learning, 81-88.
19. Zheng, Z.; Yang, F; Tan, W; Jia, J.; and Yang, J. 2007. Gabor feature-based face recognition using supervised locality preserving projection. Signal Processing 87(10):2473-2483.