Variational inference for the Indian buffet process

Journal of Machine Learning Research

Volumn 5, Issue , 2009, Pages 137-144

  Doshi Velez, Finale ^a   Miller, Kurt T ^b   Gael, Jurgen Van ^a   The, Yee Whye ^c  

^a UNIVERSITY OF CAMBRIDGE   (United Kingdom)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c UNIVERSITY COLLEGE LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

FEATURE MODELS; INFERENCE METHODS; NON-PARAMETRIC; THEORETICAL BOUNDS; TRUNCATION ERRORS; UNOBSERVED FEATURES; VARIATIONAL INFERENCE; VARIATIONAL METHODS;

SOFTWARE ENGINEERING;

ARTIFICIAL INTELLIGENCE;

EID: 80053927588     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Conference Paper

References (12)

1. Beal, M. J. (2003). Variational algorithms for approximate bayesian inference. Doctoral dissertation, Gatsby Computational Neuroscience Unit, UCL.
2. Blei, D., & Jordan, M. (2004). Variational methods for the Dirichlet process. Proceedings of the 21st International Conference on Machine Learning.
3. Georghiades, A., Belhumeur, P., & Kriegman, D. (2001). From few to many: Illumination cone models for face recognition under variable lighting and pose. IEEE Trans. Pattern Anal. Mach. Intelligence, 23.
4. Griffiths, T., & Ghahramani, Z. (2005). Infinite latent feature models and the Indian buffet process. TR 2005-001, Gatsby Computational Neuroscience Unit.
5. Ishwaran, H., & James, L. F. (2001). Gibbs sampling methods for stick breaking priors. Journal of the American Statistical Association, 96, 161-173.
6. Knowles, D., & Ghahramani, Z. (2007). Infinite Sparse Factor Analysis and Infinite Independent Components Analysis. Lecture Notes in Computer Science, 4666, 381.
7. Meeds, E., Ghahramani, Z., Neal, R. M., & Roweis, S. T. (2007). Modeling dyadic data with binary latent factors. Advances in Neural Information Processing Systems 19.
8. Teh, Y. W., Gorur, D., & Ghahramani, Z. (2007). Stickbreaking construction for the Indian buffet process. Proceedings of the 11th Conference on Artificial Intelligence and Statistic.
9. Thibaux, R., & Jordan, M. (2007). Hierarchical beta processes and the indian buffet process. Proceedings of the International Conference on Artificial Intelligence and Statistics.
10. Wainwright, M. J., & Jordan, M. I. (2008). Graphical models, exponential families, and variational inference. Foundations and Trends in Machine Learning, 1, 1-305.
11. Winther, O. (2000). Gaussian processes for classification: Mean field algorithms. Neural Computation, 12.
12. Wood, F., & Griffiths, T. L. (2007). Particle filtering for nonparametric Bayesian matrix factorization. In Advances in Neural Information Processing Systems 19.