Multi-output regularized feature projection

IEEE Transactions on Knowledge and Data Engineering

Volumn 18, Issue 12, 2006, Pages 1600-1613

  Yu, Shipeng ^a   Yu, Kai ^b   Tresp, Volker ^b   Kriegel, Hans Peter ^a  

^a UNIVERSITY OF MUNICH   (Germany)

^b SIEMENS AG   (Germany)

Author keywords

Dimensionality reduction; Feature transformation; Supervised projection

Indexed keywords

CLASSIFICATION (OF INFORMATION); INFORMATION RETRIEVAL; MAPPING; MATHEMATICAL MODELS; STATISTICAL METHODS;

DIMENSIONALITY REDUCTION; FEATURE TRANSFORMATIONS; MULTI OUTPUT REGULARIZED FEATURE PROJECTION (MORP); SUPERVISED PROJECTION;

FEATURE EXTRACTION;

EID: 33750823079     PISSN: 10414347     EISSN: None     Source Type: Journal    
DOI: 10.1109/TKDE.2006.194     Document Type: Article

References (26)

1. T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning. Springer Verlag, 2001.
2. P.N. Belhumeur, 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.
3. S.C. Deerwester, S.T. Dumais, T.K. Landauer, G.W. Furnas, and R.A. Harshman, "Indexing by Latent Semantic Analysis," J. Am. Soc. Information Science, vol. 41, no. 6, pp. 391-407, 1990.
4. M.W. Berry, S.T. Dumais, and G.W. O'Brien, "Using Linear Algebra for Intelligent Information Retrieval," SLAM Rev., vol. 37, no. 4, pp. 573-595, 1995.
5. C. Basu, H. Hirsh, and W.W. Cohen, "Recommendation as Classification: Using Social and Content-Based Information in Recommendation," Proc. 15th Nat'l Conf. Artificial Intelligence AAAI/IAAI, pp. 714-720, 1998.
6. A.N. Tikhonov and V.Y. Arsenin, Solutions of El-Posed Problems. New York: Wiley, 1977.
7. B. Schölkopf, A. Smola, and K.-R. Müller, "Nonlinear Component Analysis as a Kernel Eigenvalue Problem," Neural Computation, vol. 10, pp. 1299-1319, 1998.
8. H. Zou, T. Hastie, and R. Tibshirani, "Sparse Principal Component Analysis," technical report, Statistics Dept., Stanford Univ., 2005.
9. V. Vapnik, The Nature of Statistical Learning Theory. New York: Springer, 1995.
10. G. H. Golub and C.F. Van Loan, Matrix Computations. The Johns Hopkins Univ. Press, 1996.
11. K. Mardia, J. Kent, and J. Bibby, Multivariate Analysis. Academic Press, 1979.
12. B. Schölkopf, A.J. Smola, and K.-R. Müller, "Kernel Principal Component Analysis," Advances in Kernel Methods - Support Vector Learning, pp. 327-352, 1999.
13. J. Shawe-Taylor and N. Cristianini, Kernel Methods for Pattern Analysis. Cambridge Univ. Press, 2004.
14. H. Hotelling, "Relations between Two Sets of Variables," Biometrika, vol. 28, pp. 321-377, 1936.
15. D.R. Hardoon, S. Szedmak, and J. Shawe- Taylor, "Canonical Correlation Analysis; An Overview with Application to Learning Methods," technical report, Royal Holloway Univ. of London, 2003.
16. H. Wold, "Soft Modeling by Latent Variables; The Nonlinear Iterative Partial Least Squares Approach," Perspectives in Probability and Statistics, Papers in Honour of M.S. Bartlett, 1975.
17. R. Rosipal and L.J. Trejo, "Kernel Partial Least Squares Regression in Reproducing Kernel Hubert Space," J. Machine Learning Research, vol. 2, no. 12, pp. 97-123, 2001.
18. J. Weston, O. Chapelle, A. Elisseeff, B. Schölkopf, and V. Vapnik, "Kernel Dependency Estimation," Advances in Neural Information Processing Systems 15, S. Thrun, S. Becker, and K. Obermayer, eds., MIT Press, 2003.
19. K. Fukumizu, F.R. Bach, and M.I. Jordan, "Dimensionality Reduction for Supervised Learning with Reproducing Kernel Hubert Spaces," J. Machine Learning Research, vol. 5, pp. 73-99, Jan. 2004.
20. T. Evgeniou and M. Pontil, "Regularized Multi-Task Learning," Proc. ACM SIGKDD, 2004.
21. K. Yu, V. Tresp, and S. Yu, "A Nonparametric Hierarchical Bayesian Framework for Information Filtering," Proc. 27th Ann. Int'l ACM SIGIR Conf., 2004.
22. A. Schwaighofer, V. Tresp, and K. Yu, "Hierarchical Bayesian Modelling with Gaussian Processes," Advances in Neural Information Processing Systems 17, MIT Press, 2005.
23. B. Schölkopf and A.J. Smola, Learning with Kernels. MIT Press, 2002.
24. K. Fukunaga, Statistical Pattern Recognition, second ed. Academic Press, 1990.
25. H. Wold, "Partial Least Squares," Encyclopedia of the Statistical Sciences, pp. 581-591, 1985.
26. D.D. Lewis, Y. Yang, T. Rose, and F. Li, "RCV1: A New Benchmark Collection for Text Categorization Research," J. Machine Learning Research, vol. 5, pp. 361-397, 2004.