Learning the β-divergence in tweedie compound poisson matrix factorization models

30th International Conference on Machine Learning, ICML 2013

Volumn , Issue PART 3, 2013, Pages 2459-2467

  Şimşekli, Umut ^a   Cemgil, Ali Taylan ^a   Yilmaz, Yusuf Kenan ^b  

^a BOGAZICI UNIVERSITY   (Turkey)

^b Sibnet Computers Ltd   (Turkey)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTER MUSIC; DISPERSIONS; ESTIMATION; FACTORIZATION; INFERENCE ENGINES; LEARNING SYSTEMS; TENSORS;

COMPOUND POISSON DISTRIBUTION; EXPONENTIAL DISPERSION MODELS; HETEROGENEOUS DATASETS; MATRIX FACTORIZATIONS; NON-NEGATIVE MATRIX AND TENSOR FACTORIZATIONS; PROBABILISTIC INTERPRETATION; SYMBOLIC REPRESENTATION; TENSOR FACTORIZATION;

PARAMETER ESTIMATION;

EID: 84897475495     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (18)

1. Bar-Lev, S. K. and Enis, P. Reproducibility and natural exponential families with power variance functions. Annals of Stat., 14, 1986.
2. Bertin-Mahieux, Thierry, Ellis, Daniel P.W., Whitman, Brian, and Lamere, Paul. The million song dataset. In Proceedings of the 12th International Conference on Music Information Retrieval (ISMIR 2011), 2011.
3. Boulanger-Lewandowski, Nicolas, Bengio, Yoshua, and Vincent, Pascal. Modeling temporal dependencies in high-dimensional sequences: Application to polyphonic music generation and transcription. In International Conference on Machine Learning (ICML), 2012.
4. Cichocki, A., Zdunek, R., Phan, A. H., and Amari, S. Nonnegative Matrix and Tensor Factorization. Wiley, 2009.
5. Şimşekli, U., Yilmaz, Y. K., and Cemgil, A. T. Score guided audio restoration via generalised coupled tensor factorization. In IEEE International Conference on Acoustics, Speech, and Signal Processing, 2012.
6. Dikmen, Onur and Févotte, Cédric. Maximum marginal likelihood estimation for nonnegative dictionarylearning in the gamma-poisson models. IEEE Transactions on Signal Processing, 60(10):5163-5175, 2012.
7. Dunn, P. K. and Smyth, G. S. Series evaluation of tweedie exponential dispersion model densities. Stats. & Comp., 15:267-280, 2005. (Pubitemid 41442615)
8. Emiya, V., Badeau, R, and David, B. Multipitch estimation of piano sounds using a new probabilistic spectral smoothness principle. IEEE TASLP, 18(6): 1643-1654, 2010.
9. Févotte, C., Bertin, N., and Durrieu, J. L. Nonnegative matrix factorization with the Itakura-Saito divergence with application to music analysis. Neural Computation, 21:793-830, 2009.
10. Jørgensen, B. The Theory of Dispersion Models. Chapman & Hall/CRC Monographs on Statistics & Applied Probability, 1997.
11. Lu, Zhiyun, Yang, Zhirong, and Oja, Erkki. Selecting β-divergence for nonnegative matrix factorization by score matching. In Proceedings of 22nd International Conference on Artificial Neural Networks (ICANN 2012), volume 7553 of Lecture Notes in Computer Science, pp. 419-426, Lausanne, Switzerland, 2012. Springer.
12. McCulloch, C. E. and Nelder, J. A. Generalized Linear Models. Chapman and Hall, 2nd edition, 1989.
13. Smaragdis, P. Non-negative matrix factor deconvolution; extraction of multiple sound sources from monophonic inputs. In ICA, pp. 494-499, 2004.
14. Smaragdis, P. and Brown, J. C. Non-negative matrix factorization for polyphonic music transcription. In IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, pp. 177-180, 2003.
15. Tweedie, M. C. An index which distinguishes between some important exponential families. Statistics: applications and new directions, Indian Statist. Inst., Calcutta, pp. 579-604, 1984.
16. Yilmaz, Y. K. and Cemgil, A. T. Alpha/beta divergences and tweedie models. arXiv:1209.4280 v1, 2012.
17. Yilmaz, Y. K., Cemgil, A. T., and Şimşekli, U. Generalised coupled tensor factorisation. In NIPS, 2011.
18. Zhang, Yanwei. Likelihood-based and bayesian methods for tweedie compound poisson linear mixed models. Statistics and Computing, accepted, 2012.