Kernel discriminant learning for ordinal regression

IEEE Transactions on Knowledge and Data Engineering

Volumn 22, Issue 6, 2010, Pages 906-910

  Sun, Bing Yu ^a   Li, Jiuyong ^b   Wu, Desheng Dash ^c,d   Zhang, Xiao Ming ^a   Li, Wen Bo ^a  

^a INSTITUTE OF GEOLOGY AND GEOPHYSICS   (China)

^b UNIVERSITY OF SOUTH AUSTRALIA   (Australia)

^c REYKJAVIK UNIVERSITY   (Iceland)

^d UNIVERSITY OF TORONTO   (Canada)

Author keywords

Kernel discriminant analysis; Linear discriminant analysis; Ordinal regression

Indexed keywords

CLASSIFICATION ,; DATA CLASS; DATA SPACE; GLOBAL INFORMATIONS; HUMAN EVALUATION; KERNEL DISCRIMINANT ANALYSIS; LINEAR DISCRIMINANT ANALYSIS; ORDINAL REGRESSION; RANK CONSTRAINTS; REGRESSION METHOD;

ALGORITHMS; COMPUTATIONAL COMPLEXITY; COMPUTATIONAL EFFICIENCY; DISCRIMINANT ANALYSIS; FACE RECOGNITION; SUPPORT VECTOR MACHINES;

REGRESSION ANALYSIS;

EID: 77951766869     PISSN: 10414347     EISSN: None     Source Type: Journal    
DOI: 10.1109/TKDE.2009.170     Document Type: Article

References (22)

1. S. Kramer, G. Widmer, B. Pfahringer, and M. De Groeve, "Prediction of Ordinal Classes Using Regression Trees, " Fundamenta Informaticae, vol. 47, nos. 1/2, pp. 1-13, 2001. (Pubitemid 33084530)
2. R. H. Erbrich, T. Graepel, and K. Obermayer, "Large Margin Rank Boundaries for Ordinal Regression, " Advances in Large Margin Classifiers, pp. 115-132, MIT Press, 2000.
3. K. Crammer and Y. Singer, "Pranking with Ranking, " Advances in Neural Information Processing Systems, T. G. Dietterich, S. Becker, and Z. Ghahramani, eds., vol. 1, no. 14, pp. 641-647, MIT Press, 2002.
4. A. Shashua and A. Levin, "Ranking with Large Margin Principle: Two Approaches, " Advances in Neural Information Processing Systems, vol. 15, pp. 961-968, MIT Press, 2003.
5. W. Chu and S. S. Keerthi, "New Approaches to Support Vector Ordinal Regression, " Proc. 22nd Int'l Conf. Machine Learning (ICML '05), pp. 145-152, 2005. (Pubitemid 43183327)
6. L. Lin and H.-T. Lin, "Ordinal Regression by Extended Binary Classification, " Advances in Neural Information Processing Systems, vol. 19, pp. 865-872, MIT Press, 2007.
7. J. S. Cardoso and J. F. Pinto da Costa, "Learning to Classify Ordinal Data: The Data Replication Method, " J. Machine Learning Research, vol. 8, pp. 1393-1429, 2007. (Pubitemid 47210277)
8. C. M. Bishop, Pattern Recognition and Machine Learning. Springer, 2006.
9. R. O. Duda, P. E. Hart, and D. Stork, Pattern Classification. Wiley, 2000.
10. S. Mika, "Kernel Fisher Discriminants, " PhD thesis, Univ. of Technology, 2002.
11. P. N. Belhumeour, J. P. Hespanha, and D. J. Kriegman, "Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection, " IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 711-720, July 1997.
12. M. A. Turk and A. P. Pentland, "Face Recognition Using Eigenfaces, " Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 586-591, 1991.
13. X. He, S. Yan, Y. Hu, P. Niyogi, and H. Zhang, "Face Recognition Using Laplacian Faces, " IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 27, no. 3, pp. 328-340, Mar. 2005. (Pubitemid 40377326)
14. S. Yan, D. Xu, B. Zhang, H. Zhang, Q. Yang, and S. Lin, "Graph Embedding and Extensions: A General Framework for Dimensionality Reduction, " IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 29, no. 1, pp. 40-51, Jan. 2007. (Pubitemid 46415944)
15. J. Lu, K. N. Plataniotis, and A. N. Venetsanopoulos, "Face Recognition Using Kernel Direct Discriminant Analysis Algorithms, " IEEE Trans. Neural Networks, vol. 14, no. 1, pp. 117-126, Jan. 2003.
16. J. Ye, R. Janardan, and Q. Li, "Two-Dimensional Linear Discriminant Analysis, " Advances in Neural Information Processing Systems, pp. 1569-1576, MIT Press, 2004.
17. D. Cai, X. He, and J. Han, "SRDA: An Efficient Algorithm for Large-Scale Discriminant Analysis, " IEEE Trans. Knowledge and Data Eng., vol. 20, no. 1, pp. 1-12, Jan. 2008. (Pubitemid 350197262)
18. Y. Guo, T. Hastie, and R. Tibshirani, "Regularized Linear Discriminant Analysis and Its Application in Microarrays, " Biostatistics, vol. 8, no. 1, pp. 86-100, 2007. (Pubitemid 44906104)
19. H. Kim, B. Drake, and H. Park, "Adaptive Nonlinear Discriminant Analysis by Regularized Minimum Squared Errors, " IEEE Trans. Knowledge and Data Eng., vol. 18, no. 5, pp. 603-612, May 2006.
20. V. Vapnik, The Nature of Statistical Learning Theory. Wiley, 1998.
21. K.-R. Muller, S. Mika, G. Ratsch, K. Tsuda, and B. Scholkopf, "An Introduction to Kernel-Based Learning Algorithms, " IEEE Trans. Neural Networks, vol. 12, no. 2, pp. 181-201, Mar. 2001. (Pubitemid 32371478)
22. F. B. Francis and M. I. Jordan, "Predictive Low-Rank Decomposition for Kernel Methods, " Proc. 22nd Int'l Conf. Machine learning (ICML '05), pp. 33-40, 2005.