On bayesian PCA: Automatic dimensionality selection and analytic solution

Proceedings of the 28th International Conference on Machine Learning, ICML 2011

Volumn , Issue , 2011, Pages 497-504

  Nakajima, Shinichi ^a   Sugiyama, Masashi ^b   Babacan, Denn ^c  

^a NIKON CORPORATION   (Japan)

^b TOKYO INSTITUTE OF TECHNOLOGY   (Japan)

^c UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ANALYTIC SOLUTION; APPROXIMATION SCHEME; BAYESIAN ESTIMATIONS; BAYESIAN FRAMEWORKS; BEST CHOICE; CLOSED FORM; EMPIRICAL BAYES; GLOBAL OPTIMAL SOLUTIONS; GLOBAL SOLUTIONS; LATENT VARIABLE; LOADING VECTOR; MUTUALLY INDEPENDENTS; PROBABILISTIC PCA; TRIVIAL SOLUTIONS; UNIQUE FEATURES; VARIATIONAL BAYESIAN;

BAYESIAN NETWORKS; DECELERATION; LEARNING SYSTEMS;

COMPUTATIONAL EFFICIENCY;

EID: 80053452697     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (13)

1. Asuncion, A. and Newman, D.J. UCI machine learning repository, 2007.
2. Bishop, C. M. Variational principal components. ICANN1999, 1999a.
3. Bishop, C. M. Bayesian principal components. NIPS1998, 1999b.
4. Hotelling, H. Analysis of a complex of statistical variables into principal components. Journal of Educational Psychology, 24: 417-441, 1933.
5. Hoyle, D. C. Autotmaric PCA dimension selection for high dimensional data and small sample sizes. Journal of Machine Learning Research, 9:2733-2759, 2008.
6. Lim, Y. J. and Teh, T. W. Variational Bayesian approach to movie rating prediction. KDD Cup and Workshop 2007.
7. Nakajima, S., Sugiyama, M., and Tomioka, R. Global analytic solution for variational Bayesian matrix factorization. NIPS2010.
8. Neal, R. M. Bayesian Learning for Neural Networks. Springer, 1996.
9. Raiko, T, Ilin, A., and Karhunen, J. Principal component analysis for large scale problems with lots of missing values. ECML2007.
10. Roweis, S. and Ghahramani, Z. A unifying review of linear Gaussian models. Neural Computation, 11:305-345, 1999.
11. Salakhutdinov, R. and Mnih, A. Probabilistic matrix factorization. NIPS2007.
12. Srebro, N., Rennie, J., and Jaakkola, T. Maximum margin matrix factorization. NIPS2004.
13. Tipping, M. E. and Bishop, C. M. Probabilistic principal component analysis. Journal of the Royal Statistical Society, 61: 611-622, 1999.