Bayesian and L 1 approaches for sparse unsupervised learning

Proceedings of the 29th International Conference on Machine Learning, ICML 2012

Volumn 1, Issue , 2012, Pages 751-758

  Mohamed, Shakir ^a   Heller, Katherine A ^b   Ghahramani, Zoubin ^c  

^a UNIVERSITY OF BRITISH COLUMBIA   (Canada)

^b MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^c UNIVERSITY OF CAMBRIDGE   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN METHODS; BAYESIAN MODEL; COLLABORATIVE FILTERING; COMPUTATIONAL BUDGET; FACTOR MODEL; GENERALISATION; GENOMICS; LATENT VARIABLE MODELS; NUMBER OF DATUM; PREDICTIVE PERFORMANCE; REGULARISATION; SIGNAL ACQUISITIONS;

BAYESIAN NETWORKS; IMAGE CODING; SIGNAL PROCESSING;

LEARNING SYSTEMS;

EID: 84867124105     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (35)

1. E. M. Airoldi, D. M. Blei, S. E. Fienberg, and E. P. Xing. Mixed membership stochastic blockmodels. Journal of Machine Learning Research, 9:1981-2014, 2008.
2. C. Archambeau and F. Bach. Sparse probabilistic projections. In NIPS. 2009.
3. E. J. Candes. Compressive sampling. In Proc. Int. Con. Math, 2006.
4. C. Carvalho, N. Poison, and J. Scott. The horseshoe estimator for sparse signals. Biometrika, 97(2), 2010.
5. C. M. Carvalho, J. Chang, J. E. Lucas, J. R. Nevins, Q. Wang, and M. West. High-dimensional sparse factor modeling: Applications in gene expression genomics. J. Am. Stat. Ass., 103(484):1438-1456, 2008.
6. M. Collins, S. Dasgupta, and R. Schapire. A generalization of principal components to the exponential family. In NIPS 14, pages 617-624, 2002.
7. A. Courville, J. Bergstra, and Y. Bengio. A spike and slab restricted Boltzmann machine. In AISTATS 14. 2010.
8. A. d'Aspremont, L. El Ghaoui, M. I. Jordan, and G. R.G. Lanckriet. A direct formulation for sparse PCA using semidefinite programming. In NIPS 17, 2005.
9. D. Dueck and B. Frey. Probabilistic sparse matrix factorization. Technical report, Univ. Toronto, 2004.
10. T. Griffiths and Z. Ghahramani. Infinite latent feature models and the Indian Buffet Process. In NIPS 19, 2006.
11. C. Guihenneuc-Jouyaux and J. Rousseau. Laplace expansions in Markov chain Monte Carlo algorithms. J. Comp. Grap. Stats, 14(1):pp. 75-94, 2005.
12. D. Hernández Lobato, J. Hernández-Lobato, T. Helleputte, and P. Dupont. Expectation propagation for Bayesian multi-task feature selection. Machine Learning and Knowledge Discovery in Databases, pages 522-537, 2010.
13. H. Ishwaran and J. S. Rao. Spike and Slab variable selection: Frequentist and Bayesian strategies. Annals of Statistics, 33(2):730-773, 2005.
14. I. M. Johnstone and B. W. Silverman. Needles and straw in haystacks: Empirical Bayes estimates of possibly sparse sequences. Ann. Stat., 32(4):1594-1694, 2004.
15. A. Kaban and E. Bingham. ICA-based binary feature construction. In ICA 6, 2006.
16. C. Kemp and J. B. Tenenbaum. The discovery of structural form. PNAS, 105(31):10687-10692, 2008.
17. H. Lee, R. Raina, A. Teichman, and A. Y. Ng. Exponential family sparse coding with applications to self-taught learning. In IJCAI'09, pages 1113-1119, 2009.
18. S. Lee, H. Lee, P. Abbeel, and A.Y. Ng. Efficient L1 regularized logistic regression. In AAAI-06. 2006.
19. A. Levin, Y. Weiss, F. Durand, and W. T. Freeman. Understanding and evaluating blind deconvolution algorithms. In CVPR, 2009.
20. J. Lücke and A Sheikh. Closed-form EM for sparse coding and its application to source separation. In LVA/ICA, 2012.
21. D. J. C. MacKay. Information Theory, Inference & Learning Algorithms. Cambridge Press, 2003.
22. T. J. Mitchell and J. J. Beauchamp. Variable selection in linear regression (with discussion). Journal of the American Statistical Association, 83:1023-1036, 1988.
23. S. Mohamed, K. Heller, and Z. Ghahramani. Bayesian exponential family PCA. In NIPS 21, 2008.
24. R. M. Neal. Slice sampling. Ann. Stat., 31(3), 2003.
25. R. B. O'Hara and M. J. Sillanpäa. A review of Bayesian variable selections methods: What, how and which. Bayesian Analysis, 4(1):85-118, 2009.
26. B. A. Olshausen and D. J. Field. Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature, 381(6583):607-609, June 1996.
27. N. G. Poison and J. G. Scott. Shrink globally, act locally: Sparse Bayesian regularization and prediction. In Bayesian Statistics, volume 9. 2010.
28. M. Rattray, O. Stegle, K. Sharp, and J. Winn. Inference algorithms and learning theory for Bayesian sparse factor analysis. 2009.
29. M. Schmidt, G. Fung, and R. Rosales. Fast optimization methods for L1 regularization: A comparative study and two new approaches. In ECML-2006. Springer, 2007.
30. M. Seeger., F. Steinke, and K. Tsuda. Bayesian inference and optimal design in the sparse linear model. In AISTATS 11, pages 444-451, 2007.
31. R. Tibshirani. Regression shrinkage and selection via the LASSO. JRSS B, 58(1):267-288, 1996.
32. S. A. van de Geer and P. Bühlmann. On the conditions used to prove oracle results for the Lasso. Electronic Journal of Statistics, 3:1360-1392, 2009.
33. D. Wipf and S. Nagarajan. A new view of automatic relevance determination. In NIPS 20, 2008.
34. T.J. Yen. A majorization-minimization approach to variable selection using spike and slab priors. The Annals of Statistics, 39(3):1748-1775, 2011.
35. H. Zou, T. Hastie, and R. Tibshirani. Sparse PCA. Technical report, Stanford U., 2004.