Dense message passing for sparse principal component analysis

Journal of Machine Learning Research

Volumn 9, Issue , 2010, Pages 725-732

  Sharp, Kevin ^a   Rattray, Magnus ^a  

^a UNIVERSITY OF MANCHESTER   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN INFERENCE; GENE EXPRESSION DATASETS; HIGH-DIMENSIONAL; INFERENCE ALGORITHM; INFERENCE PROBLEM; MEAN-FIELD; MESSAGE PASSING ALGORITHM; MODEL PARAMETERS; NEAR-OPTIMAL PERFORMANCE; PROBABILISTIC MODELS; SPARSE CLASSIFICATION; SPARSE PRINCIPAL COMPONENT ANALYSIS; STATISTICAL PHYSICS; SYNTHETIC DATA; VARIATIONAL BAYES;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BAYESIAN NETWORKS; GENE EXPRESSION; OPTIMIZATION; PRINCIPAL COMPONENT ANALYSIS; STATISTICAL MECHANICS;

INFERENCE ENGINES;

EID: 84862288022     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Conference Paper

References (21)

1. C. Archambeau and F. Bach. Sparse probabilistic projections. In D. Koller, D. Schuurmans, Y. Bengio, and L. Bottou, editors, Advances in Neural Information Processing Systems 21, pages 73-80. 2009.
2. S. A. Armstrong, J. E. Staunton, L. B. Silverman, R. Pieters, M. L. den Boer, M. D. Minden, S. E. Sallan, E. S. Lander, T. R. Golub, and S. J. Korsmeyer. Mll translocations specify a distinct gene expression profile that distinguishes a unique leukemia. Nature Genetics, 30:41-47, 2002.
3. C. Carvalho, J. Chang, J. Lucas, J. Nevins, Q. Wang, and M. West. High-dimensional sparse factor modeling: Applications in gene expression genomics. Journal of the American Statistical Association, 103(484):1438-1456, 2008.
4. C. Carvalho, N. Polson, and J. Scott. Handling sparsity via the horseshoe. Journal of Machine Learning Research, W&CP 5:73-80, 2009.
5. Y. Guan and J. Dy. Sparse probabilistic principal component analysis. Journal of Machine Learning Research, W&CP 5:185-192, 2009.
6. I. Jolliffe. Principal component analysis. Springer Verlag, New York, 1986.
7. I. Jolliffe and M. Uddin. A modified principal component analysis technique based on the lasso. Journal of Computational and Graphical Statistics, 12:531-547, 2003.
8. T. Mitchell and J. Beauchamp. Bayesian variable selection in linear regression. Journal of the American Statistical Association, 83:1023-1032, 1988.
9. I. Pournara and L.Wernisch. Factor analysis for gene regulatory networks and transcription factor activity profiles. BMC Bioinformatics, 8:61, 2007.
10. S. Ramaswamy, P. Tamayo, R. Rifkin, S. Mukherjee, C.-H. Yeang, M. Angelo, C. Ladd, M. Reich, E. Latulippe, J. P. Mesirov, T. Poggio, W. Gerald, M. Loda, E. S. Lander, and T. R. Golub. Multiclass cancer diagnosis using tumor gene expression signatures. Proceedings of the National Academy of Sciences of the United States of America, 98(26):15149-15154, 2001.
11. M. Rattray, O. Stegle, K. Sharp, and J. Winn. Inference algorithms and learning theory for Bayesian sparse factor analysis. Journal of Physics: Conference Series, 197: 012002 (10pp), 2009.
12. P. Reimann, C. Van den Broeck, and G. J. Bex. A Gaussian scenario for unsupervised learning. Journal of Physics A, 29:3521-3535, 1996.
13. C. Sabatti and G. M. James. Bayesian sparse hidden components analysis for transcription regulation networks. Bioinformatics, 22(6):739-746, 2006.
14. C. Sigg and J. Buhmann. Expectation-maximization for sparse and non-negative PCA. In A. McCallum and S. Roweis, editors, Proceedings of the 25th Annual International Conference on Machine Learning (ICML 2008), pages 960-967. 2008.
15. R. Tibshirani. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B, 58:267-288, 1996.
16. M. E. Tipping and C. M. Bishop. Probabilistic principal component analysis. Journal of the Royal Statistical Society: Series B, 61:611-622, 1999.
17. S. Uda and Y. Kabashima. Statistical mechanical development of a sparse Bayesian classifier. Journal of the Physical Society of Japan, 74:2233-2242, 2005.
18. M. West. Bayesian factor regression models in the 'Large p, Small n' paradigm. Bayesian Statistics, 7:723-732, 2003.
19. P. Williams. Bayesian regularization and pruning using a Laplace prior. Neural Computation, 7:117-143, 1995.
20. D. Wipf and S. Nagarajan. A new view of automatic relevance determination. In J. Platt, D. Koller, Y. Singer, and S. Roweis, editors, Advances in Neural Information Processing Systems 20, pages 1625-1632. MIT Press, Cambridge, MA, 2008.
21. H. Zou, T. Hastie, and R. Tibshirani. Sparse principal component analysis. Journal of computational and graphical statistics, 1:265, 2006.