Learning with heterogenous data sets by weighted multiple kernel canonical correlation analysis

Machine Learning for Signal Processing 17 - Proceedings of the 2007 IEEE Signal Processing Society Workshop, MLSP

Volumn , Issue , 2007, Pages 81-86

  Shi, Yu ^a   De Moor, Bart ^a   Moreau, Yves ^a  

^a Katholieke Universiteit Leuven   (Belgium)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; CORRELATION METHODS; EDUCATION; EIGENVALUES AND EIGENFUNCTIONS; LEARNING SYSTEMS; ROBOT LEARNING; SIGNAL INTERFERENCE; SIGNAL PROCESSING;

CANONICAL CORRELATION ANALYSIS; COMMON INFORMATION; COMPUTATIONAL ISSUES; DATA SETS; EIGEN VALUE PROBLEMS; EIGEN VALUES; EIGENVALUE DECOMPOSITION METHOD; HETEROGENEOUS DATA; HETEROGENOUS DATA; IEEE SIGNAL PROCESSING SOCIETY; INCOMPLETE CHOLESKY DECOMPOSITION; LARGE DATA SETS; MACHINE-LEARNING;

SINGULAR VALUE DECOMPOSITION;

EID: 48149093697     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/MLSP.2007.4414286     Document Type: Conference Paper

References (11)

1. H. Hotelling, "Relations between two sets of variates," Biometrika, vol. 28, pp. 321-377, 1936.
2. J. R. Kettenring, "Canonical analysis of several sets of variables," Biometrika, vol. 58, pp. 433-451, 1971.
3. S. Akaho, "A kernelmethod for canonical correlation analysis," in International Meeting of Psychometric Society (IMPS2001), 2001.
4. F. R. Bach and M. I. Jordan, "Kernel independent component analysis," J. Mach. Learn. Res., vol. 3, pp. 1-48, 2003.
5. D. R. Hardoon, S. R. Szedmak, and J. R. Shawe-taylor, "Canonical correlation analysis: An overview with application to learning methods," Neural. Comput., vol. 16, no. 12, pp. 2039-2664, 2004.
6. A. Gretton, R. Herbrich, A. Smola, O. Bousquet, and B. Scholkopf, "Kernel methods for measuring independence," J. Mach. Learn. Res., vol. 6, pp. 2075-2129, 2005.
7. Y. Bengio, O. Delalleau, N. Le Roux, J. F. Paiement, P. Vincent, and M. Ouimet, "Learning eigenfunctions links spectral embedding and kernel PCA," Neural Computation, vol. 16, no. 10, pp. 2197-2219, 2004.
8. Y. I. Chang, Y. J. Lee, H. K. Pao, M. Lee, and S. Y. Huang, "Data visualization via kernel machines," Tech. Rep., Institute of Statistical Science, Academia Sinica, Taiwan, China, 2004.
9. H. C. Kim, S. Pang, H. M. Je, D. Kim, and S. Y. Bang, "Pattern classification using support vector machine ensemble," Pattern Recognition, 2002. Proceedings. 16th International Conference on, vol. 2, pp. 160-163, 2002.
10. A. L. I. Oliveira, F. B. L. Neto, and S. R. L. Meira, "Improving rbf-dda performance on optical character recognition through parameter selection," in ICPR '04, Washington, DC, USA, 2004, pp. 625-628, IEEE Computer Society.
11. L. Holmstrom and F. Hoti, "Application of semiparametric density estimation to classification," in ICPR '04, Washington, DC, USA, 2004, pp. 371-374, IEEE Computer Society.