Reading list [09/07/27-09/08/28]

Simultaneous dimension direction ction and regression or supervised dimension direction ction (SDR) Methods

Outline:

29 July 2009 
Kai Mao, Qiang Wu, Feng Liang, Sayan Mukherjee. Two Models for Bayesian supervised Dimension Modeling ction.
Presenter: Xinwei Jiang, presentation material will come soon.

29 July 2009 
Wu, Q., J. guinney, M. maggioni, and S. Mukherjee (2007). Learning gradients: predictivemodels that infer geometry and dependence. Technical Report 07, Duke University.
Presenter: Xinwei Jiang, presentation material will come soon.

Others:

1. Vlassis, N., Y. Motomura, and B. Kr? ose (2001). Supervised dimension compression ction of intrinsically low-dimen1_data. Neural computation, 191-215.
2. fukumizu, K., F. Bach, and M. Jordan (2003). kernel dimensionality modeling ction for supervised learning. In advances in neural information processing systems 16.
3. Li, B., H. Zha, and F. chiaromonte (2004). Linear contour Learning: A method for supervised Dimension Modeling. pp. 346-356. UAI.
Goldberger, J., S. roweis, G. Hinton, and R. salakhudinov (2005). neighbourhood component analysis. In advances in neural information processing systems 17, pp. 513-520.
4. fukumizu, K ., f. bach, and M. jordan (1, 2005 ). dimensionality functions in supervised learning with reproducing kernel Hilbert spaces. journal of machine learning research 5, 73-99.
5. Globerson, A. and S. roweis (2006). Metric learning by collapsing classes. In advances in neural information processing systems 18, pp. 451-458.
6. martin-m'erino, M. and J. r'oman (2006 ). A new semi-supervised dimension ction technique for textual data analysis. in Intelligent Data Engineering and automatic learning.
7. Nilsson, J., F. Sha, and M. Jordan (2007). regression on manifolds Using Kernel dimension ction. In Proceedings of the 24th International Conference on machine learning.

Methods Based on gradients of the regression function:

1. xia, Y ., h. tong, W. li, and L.-X. zhu( 2002 ). an adaptive estimation of dimension limit ction space. j. roy. statist. soc. ser. B 64 (3), 363-410.
2. Mukherjee, S. and D. Zhou (2006). Learning coordinate covariances via gradients. J. Mach. Learn. res. 7,519-549.
3. Mukherjee, S. and Q. Wu (2006). Estimation of gradients and coordinate covariation in classification. J. Mach. Learn. res. 7, 2481-2514.
4. Mukherjee, S., Q. Wu, and D.-X. Zhou (2009). Learning gradients and Feature Selection on manifolds.

Methods Based on Inverse Regression:

1. Li, K. (1991). Sliced Inverse Regression for dimension ction. J. Amer. statist. Assoc. 86,316-342.
2. Cook, R. and S. Weisberg (1991). Discussion of "Sliced Inverse Regression for dimension ction". j. Amer. statist. Assoc. 86,328-332.
3. Sugiyama, M. (2007). dimensionality allocation ction of multimodal labeled data by local Fisher Discriminant Analysis. J. Mach. Learn. res. 8, 1027-1061.
4. Cook, R. (2007). Fisher Lecture: dimension loss ction in regression. Statistical Science 22 (1), 1-26.

Methods Based on forward regression:

1. Friedman, J. H. and W. stuetzle (1981). Projection pursuit regression. J. Amer. statist. Assoc., 817-823.
2. tokdar, S., Y. Zhu, and J. Ghosh (2008). A Bayesian Implementation of sufficient dimension partition ction in regression. Technical Report, Purdue Univ.