Bayesian sparse partial least squares

Neural Computation

Volumn 25, Issue 12, 2013, Pages 3318-3339

  Vidaurre, Diego ^a   Van Gerven, Marcel A J ^b   Bielza, Concha ^c   Larrañaga, Pedro ^c   Heskes, Tom ^d  

^a UNIVERSITY OF OXFORD   (United Kingdom)

^b RADBOUD UNIVERSITY   (Netherlands)

^c UNIVERSIDAD POLITÉCNICA DE MADRID   (Spain)

^d RADBOUD UNIVERSITY NIJMEGEN   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ANIMAL; BAYES THEOREM; BRAIN; ELECTROENCEPHALOGRAPHY; HAPLORHINI; LETTER; MOVEMENT (PHYSIOLOGY); PHYSIOLOGY; REGRESSION ANALYSIS; SIGNAL PROCESSING;

ALGORITHMS; ANIMALS; BAYES THEOREM; BRAIN; ELECTROENCEPHALOGRAPHY; HAPLORHINI; LEAST-SQUARES ANALYSIS; MOVEMENT; SIGNAL PROCESSING, COMPUTER-ASSISTED;

EID: 84887564612     PISSN: 08997667     EISSN: 1530888X     Source Type: Journal    
DOI: 10.1162/NECO_a_00524     Document Type: Letter

References (29)

1. Ando, R., & Zhang, T. (2005). A framework for learning predictive structures from multiple tasks and unlabeled data. Journal of Machine Learning Research, 6, 1817-1853.
2. Argyriou, A., Evgeniou, T., & Pontil, M. (2006). Multi-task feature learning. In B. Schölkopf, B. Platt, & T.Hoffmann (Eds.), Advances in neural information processing systems, 19 (pp. 41-48). Cambridge, MA: MIT Press.
3. Barker, M., & Rayens, W. (2003). Partial least squares for discrimination. Journal of Chemometrics, 17, 166-173.
4. Beal,M. J. (2003). Variational algorithms for approximate Bayesian inference. Unpublished doctoral dissertation, University College London.
5. Bishop, C. (1998). Bayesian principal components. In M. S. Keams, S. Sola, & D. A Cohn (Eds.), Advances in neural information processing systems, 11. Cambridge,MA: MIT Press.
6. Brown, P. J., & Zidek, J. V. (1980). Adaptive multivariate ridge regression. Annals of Statistics, 8, 64-74.
7. Chun, H., & Keleş, S. (2010). Sparse partial least squares regression for simultaneous dimension reduction and variable selection. Journal of the Royal Statistical Society: Series B, 72, 3-25.
8. de Jong, S. (1993). SIMPLS: An alternative approach to partial least squares regression. Chemometrics and Intelligent Laboratory Systems, 18, 251-263.
9. Frank, I. E., & Friedman, J. H. (1993). A statistical view of some chemometrics regression tools. Technometrics, 35, 109-135.
10. Fujiwara, Y., Miyawaki, Y., & Kamitani, Y. (2009). Estimating image bases for visual image reconstruction from human brain activity. In Y. Bengio, P. Schuurmans, J. Lafferty, C.Williams,& A. Culotta (Eds.), Advances in neural information processing systems, 22. Cambridge, MA: MIT Press.
11. Ghahramani, Z., & Beal, M. J. (2000). Variational inference for Bayesian mixtures of factor analysers. In S. A. Solla, T. K. Leen,&K.-R.Müller (Eds.), Advances in neural information processing systems, 12. Cambridge, MA: MIT Press.
12. Goutis,C. (1996). Partial least squares yields shrinkage estimators. Annals of Statistics, 24, 816-824.
13. Hardoon, D. R., Szedmak, S., & Shawe-Taylor, J. (2004). Canonical correlation analysis: An overview with application to learning methods. Neural Computation, 16, 2639-2664.
14. Hastie, T., Tibshirani, R., & Friedman, J. (2008). The elements of statistical learning: Data mining, inference, and predictions (2nd ed.). New York: Springer.
15. Izenman, A. (1975). Reduced-rank regression for the multivariate linearmodel. Journal of Multivariate Analysis, 5, 248-264.
16. Jaakkola, T. S. (2001). Tutorial on variational approximation methods. In M. Opper & D. Saad (Eds.), Advanced mean field methods: Theory and practice. Cambridge, MA: MIT Press.
17. Lindgren, F., Geladi, P., & Wold, S. (1993). The kernel algorithm for PLS. Journal of Chemometrics, 7, 45-59.
18. Nakajima, S., Sugiyama, M., & Babacan, D. (2011). On Bayesian PCA: Automatic dimensionality selection and analytic solution. In 37th International Conference on Machine Learning. International Machine Learning Society.
19. Obozinski, G., Wainwright, M. J., & Jordan, M. I. (2011). Support union recovery in high-dimensional multivariate regression. Annals of Statistics, 39, 1-47.
20. Rosipal, R., & Krämer, N. (2006). Overview and recent advances in partial least squares. Lecture Notes in Computer Science, 3940, 34-51.
21. Rosipal, R.,&Trejo, L. J. (2001). Kernel partial least squares regression in reproducing kernel Hilbert space. Journal of Machine Learning Research, 2, 97-123.
22. Rothman, A. J., Levina, E., & Zhu, J. (2010). Sparse multivariate regression with covariance estimation. Journal of Computational and Graphical Statistics, 19, 947-962.
23. van Gerven, M. A. J., Chao, Z. C., & Heskes, T. (2012). On the decoding of intracranial data using sparse orthonormalized partial least squares. Journal of Neural Engineering, 9, 026017.
24. Virtanen, S., Klami, A., & Kaski, S. (2011). Bayesian CCA via group sparsity. In 37th International Conference onMachine Learning. International Machine Learning Society.
25. Wang, C. (2007). Variational Bayesian approach to canonical correlation analysis. IEEE Transactions on Neural Networks, 18, 905-910.
26. Wangen, L. E.,&Kowalsky, B. R. (1989). Amultiblock partial least squares algorithm for investigating complex chemical systems. Journal of Chemometrics, 3, 3-20.
27. Wold, H. (1975). Path models with latent variables: The NIPALS approach. In H. M. Blalock (Ed.), Quantitative sociology: International perspectives on mathematical and statistical model building (pp. 307-357). New York: Academic Press.
28. Wold, S., Sjöström, M., & Eriksson, L. (2001). PLS-regression: A basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems, 58, 109-130.
29. Yuan,M., Ekici, A., Lu, Z.,&Monteiro,R. (2007). Dimension reduction and coefficient estimation in multivariate linear regression. Journal of the Royal Statistical Society: Series B, 69, 329-346.