Kernelized Bayesian Matrix Factorization

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 36, Issue 10, 2014, Pages 2047-2060

  Gonen, Mehmet ^a   Kaski, Samuel ^b,c  

^a SAGE BIONETWORKS   (United States)

^b AALTO UNIVERSITY   (Finland)

^c UNIVERSITY OF HELSINKI   (Finland)

Author keywords

Automatic relevance determination; biological interaction networks; large margin learning; matrix factorization; multilabel classification; multiple kernel learning; multiple output regression; variational approximation

Indexed keywords

BAYESIAN NETWORKS; CLASSIFICATION (OF INFORMATION); FACTORIZATION; REGRESSION ANALYSIS;

AUTOMATIC RELEVANCE DETERMINATION; BIOLOGICAL INTERACTIONS; LARGE-MARGIN LEARNING; MATRIX FACTORIZATIONS; MULTI-LABEL CLASSIFICATIONS; MULTIPLE KERNEL LEARNING; MULTIPLE OUTPUTS; VARIATIONAL APPROXIMATION;

MATRIX ALGEBRA;

ALGORITHM; AUTOMATED PATTERN RECOGNITION; BAYES THEOREM; COMPUTER SIMULATION; MACHINE LEARNING; MATHEMATICAL COMPUTING; PROCEDURES; PROTEIN ANALYSIS; STATISTICAL ANALYSIS; STATISTICAL MODEL;

ALGORITHMS; BAYES THEOREM; COMPUTER SIMULATION; DATA INTERPRETATION, STATISTICAL; MACHINE LEARNING; MODELS, STATISTICAL; NUMERICAL ANALYSIS, COMPUTER-ASSISTED; PATTERN RECOGNITION, AUTOMATED; PROTEIN INTERACTION MAPPING;

EID: 84948569682     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPAMI.2014.2313125     Document Type: Article

References (51)

1. N. Srebro, "Learning with matrix factorizations," Ph.D. dissertation, Massachusetts Inst. Tech., Cambridge, MA, USA, 2004.
2. R. Salakhutdinov and A. Mnih, "Probabilistic matrix factorization," in Proc. Adv. Neural Inf. Process. Syst., 2008, pp. 1257-1264.
3. R. Salakhutdinov and A. Mnih, "Bayesian probabilistic matrix factorization using Markov chain Monte Carlo," in Proc. 25th Int. Conf. Mach. Learn., 2008, pp. 880-887.
4. H. Ma, H. Yang, M. R. Lyu, I. King, "SoRec: Social recommendation using probabilistic matrix factorization," in Proc. 17th ACM Conf. Inf. Knowl. Manage., 2008, pp. 931-940.
5. D. Agarwal and B.-C. Chen, "Regression-based latent factor models," in Proc. 15th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, 2009, pp. 19-28.
6. H. Shan and A. Banerjee, "Generalized probabilistic matrix factorizations for collaborative filtering," in Proc. IEEE Int. Conf. Data Mining, 2010, pp. 1025-1030.
7. D. Agarwal and B.-C. Chen, "fLDA: Matrix factorization through latent Dirichlet allocation," in Proc. 3rd ACM Int. Conf. Web Search Data Mining, 2010, pp. 91-100.
8. C. Wang and D. M. Blei, "Collaborative topic modeling for recommending scientific articles," in Proc. 17th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, 2011, pp. 448-456.
9. J. Yoo and S. Choi, "Bayesian matrix co-factorization: Variational algorithm and Cram-er-Rao bound," in Proc. Eur. Conf. Mach. Learn. Principles Practice Knowl. Discovery Databases, Part III, 2011, pp. 537-552.
10. S. Park, Y.-D. Kim, S. Choi, "Hierarchical Bayesian matrix factorization with side information," in Proc. 23rd Int. Joint Conf. Artif. Intell., 2013, pp. 1593-1599.
11. K. T. Miller, T. L. Griffiths, M. I. Jordan, "Nonparametric latent feature models for link prediction," in Proc. Adv. Neural Inf. Process. Syst., 2009, pp. 1276-1284.
12. A. K. Menon and C. Elkan, "Link prediction via matrix factorization," in Proc. Eur. Conf. Mach. Learn. Principles Practice Knowl. Discovery Databases, Part II, 2011, pp. 437-452.
13. L. Zhang, D. Agarwal, B.-C. Chen, "Generalizing matrix factorization through flexible regression priors," in Proc. 5th ACM Conf. Recommender Syst., 2011, pp. 13-20.
14. W. Chu, V. Sindhwani, Z. Ghahramani, S. S. Keerthi, "Relational learning with Gaussian processes," in Proc. Adv. Neural Inf. Process. Syst., 2007, pp. 289-296.
15. J. Luttinen and A. Ilin, "Variational Gaussian-process factor analysis for modeling spatio-temporal data," in Proc. Adv. Neural Inf. Process. Syst., 2009, pp. 1177-1185.
16. M. N. Schmidt and H. Laurberg, "Nonnegative matrix factorization with Gaussian process priors," Comput. Intell. Neurosci., vol. 2008, pp. 1-10, 2008.
17. M. N. Schmidt, "Function factorization using warped Gaussian processes," in Proc. 26th Int. Conf. Mach. Learn., 2009, pp. 921-928.
18. N. D. Lawrence and R. Urtasun, "Non-linear matrix factorization with Gaussian processes," in Proc. 26th Int. Conf. Mach. Learn., 2009, pp. 601-608.
19. R. Adams, G. Dahl, I. Murray, "Incorporating side information in probabilistic matrix factorization with Gaussian processes," in Proc. 26th Conf. Annu Conf. Uncertainity Artif. Intell., 2010, pp. 1-9.
20. T. Zhou, H. Shan, A. Banerjee, G. Sapiro, "Kernelized probabilistic matrix factorization: Exploiting graphs and side information," in Proc. SIAM Int. Conf. Data Mining, 2012, pp. 403-414.
21. Y. Yamanishi, "Supervised bipartite graph inference," in Proc. Adv. Neural Inf. Process. Syst., 2009, pp. 1841-1848.
22. A. Ben-Hur and W. S. Noble, "Kernel methods for predicting protein-protein interactions," Bioinformatics, vol. 21, no. suppl. 1, pp. i38-i46, 2005.
23. Y. Yamanishi, M. Araki, A. Gutteridge, W. Honda, M. Kaneisha, "Prediction of drug-target interaction networks from the integration of chemical and genomic spaces," Bioinformatics, vol. 24, pp. i232-i240, 2008.
24. Y. Yamanishi, M. Kotera, M. Kanesiha, S. Goto, "Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework," Bioinformatics, vol. 26, pp. i246-i254, 2010.
25. Kernel Methods in Computational Biology, B. Scholkopf, K. Tsuda, J.-P. Vert, eds., Cambridge, MA, USA: MIT Press, 2004.
26. M. Gonen and E. Alpayd n, "Multiple kernel learning algorithms," J. Mach. Learn. Res., vol. 12, pp. 2211-2268, 2011.
27. M. Gonen, S. A. Khan, S. Kaski, "Kernelized Bayesian matrix factorization," in Proc. 30th Int. Conf. Mach. Learn., 2013, pp. 864-872.
28. B. Scholkopf and A. J. Smola, Learning with Kernels: Support Vector Machines, Regularization, Optimization, Beyond. Cambridge, MA, USA: MIT Press, 2002.
29. M. Gonen, "Predicting drug-target interactions from chemical and genomic kernels using Bayesian matrix factorization," Bioinformatics, vol. 28, no. 18, pp. 2304-2310, 2012.
30. J. H. Albert and S. Chib, "Bayesian analysis of binary and polychotomous response data," J. Amer. Statist. Assoc., vol. 88, no. 422, pp. 669-679, 1993.
31. N. D. Lawrence and M. I. Jordan, "Semi-supervised learning via Gaussian processes," in Proc. Adv. Neural Inf. Process. Syst., 2005, pp. 753-760.
32. M. J. Beal, "Variational algorithms for approximate Bayesian inference," Ph.D. dissertation, The Gatsby Comput. Neurosci. Unit, Univ. College London, London, U.K., 2003.
33. M. Gonen, "Bayesian efficient multiple kernel learning," in Proc. 29th Int. Conf. Mach. Learn., 2012, pp. 1-8.
34. M. Girolami and S. Rogers, "Hierarchic Bayesian models for kernel learning," in Proc. 22nd Int. Conf. Mach. Learn., 2005, pp. 241-248.
35. T. Damoulas and M. A. Girolami, "Probabilistic multi-class multikernel learning: On protein fold recognition and remote homology detection," Bioinformatics, vol. 24, no. 10, pp. 1264-1270, 2008.
36. C. K. I. Williams and C. E. Rasmussen, "Gaussian processes for regression," in Proc. Adv. Neural Inf. Process. Syst., 1996, pp. 514-520.
37. I. Murray and R. P. Adams, "Slice sampling covariance hyperparameters of latent Gaussian models," in Proc. Adv. Neural Inf. Process. Syst., 2010, pp. 1732-1740.
38. M. Filippone, M. Zhong, M. Girolami, "A comparative evaluation of stochastic-based inference methods for Gaussian process models," Mach. Learn., vol. 93, no. 1, pp. 93-114, 2013.
39. R. M. Neal, Bayesian Learning for Neural Networks, New York, NY, USA: Springer, 1996.
40. V. Y. F. Tan and C. F-evotte, "Automatic relevance determination in nonnegative matrix factorization with the b-divergence," IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 7, pp. 1592-1605, Jul. 2013.
41. S. A. Khan, A. Faisal, J. P. Mpindi, J. A. Parkkinen, T. Kalliokoski, A. Poso, O. P. Kallioniemi, K. Wennerberg, S. Kaski, "Comprehensive data-driven analysis of the impact of chemoinformatic structure on the genome-wide biological response profiles of cancer cells to 1159 drugs," BMC Bioinformat., vol. 13, p. 112, 2012.
42. A. Duran, G. C. Martinez, M. Pastor, "Development and validation of AMANDA, a new algorithm for selecting highly relevant regions in molecular interaction fields," J. Chem. Inf. Model., vol. 48, no. 9, pp. 1813-1823, 2008.
43. G. Cruciani, M. Pastor, W. Guba, "VolSurf: A new tool for the pharmacokinetic optimization of lead compounds," Eur. J. Pharmaceutical Sci., vol. 11, no. suppl. 2, pp. S29-S39, 2000.
44. A. Elisseeff and J. Weston, "A kernel method for multi-labelled classification," in Proc. Adv. Neural Inf. Process. Syst., 2002, pp. 681-687.
45. M.-L. Zhang and Z.-H. Zhou, "ML-KNN: A lazy learning approach to multi-label learning," Pattern Recog., vol. 40, no. 7, pp. 2038-2048, 2007.
46. L. Tang, J. Chen, J. Ye, "On multiple kernel learning with multiple labels," in Proc. 19th Int. Joint Conf. Artif. Intell., 2009, pp. 1255-1260.
47. J. Petterson and T. Caetano, "Reverse multi-label learning," in Proc. Adv. Neural Inf. Process. Syst., 2010, pp. 1912-1920.
48. W. Zhang, X. Xue, J. Fan, X. Huang, B. Wu, M. Liu, "Multikernel multi-label learning with max-margin concept network," in Proc. 22nd Int. Joint Conf. Artif. Intell., 2012, pp. 1615-1620.
49. P. T. Spellman, G. Sherlock, M. Q. Zhang, V. R. Iyer, K. Anders, M. B. Eisen, P. O. Brown, D. Botsein, B. Futcher, "Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization," Mol. Biol. Cell, vol. 9, no. 12, pp. 3273-3297, 1998.
50. T. I. Lee, N. J. Rinaldi, F. Robert, D. T. Odom, Z. Bar-Joseph, G. K. Gerber, N. M. Hannett, C. T. Harbison, C. M. Thompson, I. Simon, J. Zeitlinger, E. G. Jennings, H. L. Murray, D. B. Gordon, B. Ren, J. J. Wyrick, J. B. Tagne, T. L. Volkert, E. Fraenkel, D. K. Gifford, R.A. Young, "Transcriptional regulatory networks in Saccharomyces cerevisiae," Science, vol. 298, no. 5594, pp. 799-804, 2002.
51. H. Chun and S. Kelęs , "Sparse partial least squares regression for simultaneous dimension reduction and variable selection," J. Royal Statist. Soc.: Ser. B (Statist. Methodol.), vol. 72, pp. 3-25, 2010.