A least squares formulation for canonical correlation analysis

Proceedings of the 25th International Conference on Machine Learning

Volumn , Issue , 2008, Pages 1024-1031

  Sun, Liang ^a   Ji, Shuiwang ^a   Ye, Jieping ^a  

^a Arizona State University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); CLUSTERING ALGORITHMS; MACHINE LEARNING; SUPERVISED LEARNING; CURVE FITTING; LEARNING SYSTEMS; LEAST SQUARES APPROXIMATIONS; ROBOT LEARNING; STRUCTURAL LOADS;

1-NORM REGULARIZATIONS; CANONICAL CORRELATION ANALYSIS; DIMENSIONALITY REDUCTION; EQUIVALENCE RELATIONSHIP; HIGH DIMENSIONAL DATA; LEAST SQUARES PROBLEMS; LEAST-SQUARES FORMULATION; MULTI LABEL CLASSIFICATION;

CORRELATION METHODS; LABELS;

CANONICAL CORRELATION ANALYSIS; CLASS LABELS; DATA SETS; DIMENSIONAL SPACES; DIMENSIONALITY REDUCTIONS; EQUIVALENCE RELATIONSHIPS; HIGH-DIMENSIONAL DATUM; LEAST SQUARES PROBLEMS; LEAST-SQUARES FORMULATIONS; MULTI LABELS;

EID: 56449106936     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1390156.1390285     Document Type: Conference Paper

References (22)

1. Bach, F. R., & Jordan, M. I. (2003). Kernel independent component analysis. J. Mach. Learn. Res., 3, 1-48.
2. Belkin, M., Niyogi, P., & Sindhwani, V. (2006). Manifold regularization: A geometric framework for learning from labeled and unlabeled examples. J. Mach. Learn. Res., 7, 2399-2434.
3. Bishop, C. M. (2006). Pattern recognition and machine learning. New York: Springer.
4. Boutell, M. R., Luo, J., Shen, X., & Brown, C. M. (2004). Learning multi-label scene classification. Pattern Recognition, 37, 1757-1771.
5. d'Aspremont, A., Ghaoui, L., Jordan, M., & Lanckriet, G. (2004). A direct formulation for sparse PCA using semidefinite programming. NIPS (pp. 41-48).
6. Donoho, D. (2006). For most large underdetermined systems of linear equations, the minimal 11-norm near-solution approximates the sparsest near-solution. Communications on Pare and Applied Mathematics, 59, 907-934.
7. Duda, R. O., Hart, P. E., & Stork, D. G. (2000). Pattern classification. New York: John Wiley and Sons, Inc.
8. Efron, B., Hastie, T., Johnstone, I., & Tibshirani, R. (2004). Least angle regression. Annals of Statistics, 32, 407.
9. Golub, G. H., & Loan, C. F. V. (1996). Matrix computations. Baltimore, MD: Johns Hopkins Press.
10. Hardoon, D. R., Szedmak, S. R., & Shawe-taylor, J. R. (2004). Canonical correlation analysis: An overview with application to learning methods. Neural Comput., 16, 2639-2664.
11. Hastie, T., Buja, A., & Tibshirani, R. (1995). Penalized discriminant analysis. Annals of Statistics, 23, 73-102.
12. Hastie, T., Tibshirani, R., & Friedman, J. H. (2001). The elements of statistical learning: Data mining, inference, and prediction. New York: Springer.
13. Hoerl, A. E., & Kennard, R. W. (1970). Ridge regression: Biased estimation for nonorthogonal problems. Technometrics, 12, 55-67.
14. Hotelling, H. (1936). Relations between two sets of variables. Biometrika, 28, 312-377.
15. Schölkopf, B., & Smola, A. J. (2002). Learning with kernels: support vector machines, regularization, optimization, and beyond. Cambridge, MA: MIT Press.
16. Sriperumbudur, B. K., Torres, D. A., & Lanckriet, G. R. G. (2007). Sparse eigen methods by D.C. programming. ICML (pp. 831-838).
17. Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. J. R. Stat. Soc. B, 58, 267-288.
18. Tomancak, P., & et al. (2002). Systematic determination of patterns of gene expression during Drosophila embryogenesis. Genome Biology, 3.
19. Vert, J.-P., & Kanehisa, M. (2003). Graph-driven feature extraction from mieroarray data using diffusion kernels and kernel cca. NIPS (pp. 1425-1432).
20. Ye, J. (2007). Least squares linear discriminant analysis. ICML (pp. 1087-1094).
21. Yu, S., Yu, K., Tresp, V., & Kriegel, H.-P. (2006). Multi-output regularized feature projection. IEEE Trans. Knowl. Data Eng., 18, 1600-1613.
22. Zhu, J., Rosset, S., Hastie, T., & Tibshirani, R. (2003). 1-norm support vector machines. NIPS (pp. 49-56).