Convex co-embedding

Proceedings of the National Conference on Artificial Intelligence

Volumn 3, Issue , 2014, Pages 1989-1996

  Mirzazadeh, Farzaneh ^a   Guo, Yuhong ^b   Schuurmans, Dale ^a  

^a UNIVERSITY OF ALBERTA   (Canada)

^b Temple University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84908178858     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (41)

1. Akata, Z.; Perronnin, F.; Harchaoui, Z.; and Schmid, C. 2013. Label-embedding for attribute-based classification. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
2. Argyriou, A.; Evgeniou, T.; and Pontil, M. 2008. Convex multitask feature learning. Machine Learning 73(3):243-272.
3. Bennett, J., and Lanning, S. 2007. The Netflix Prize. In Proceedings of KDD Cup and Workshop 2007.
4. Bleakley, K.; Biau, G.; and Vert, J. 2007. Supervised reconstruction of biological networks with local models. In International Conference on Intelligent Systems for Molecular Biology (ISMB).
5. Bordes, A.; Weston, J.; Collobert, R.; and Bengio, Y. 2011. Learning structured embeddings of knowledge bases. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI).
6. Bordes, A.; Usunier, N.; Garcia-Duran, A.; Weston, J.; and Yakhnenko, O. 2013. Translating embeddings for modeling multirelational data. In Neural Information Processing Systems (NIPS).
7. Cai, J.; Candes, E.; and Shen, Z. 2008. A singular value thresholding algorithm for matrix completion. SIAM J Optim 20:1956-1982.
8. Chen, S.; Lyu, M. R.; King, I.; and Xu, Z. 2013. Exact and stable recovery of pairwise interaction tensors. In Advances in Neural Information Processing Systems (NIPS).
9. Chopra, S.; Hadsell, R.; and Le Cun, Y. 2005. Learning a similarity metric discriminatively, with application to face verification. In IEEE Conf. Computer Vision and Patt. Recogn. (CVPR), 539-546.
10. Cortes, C., and Mohri, M. 2003. AUC optimization vs. error rate minimization. In Proceedings of Advances in Neural Information Processing Systems (NIPS).
11. Dudik, M.; Harchaoui, Z.; and Malick, J. 2012. Lifted coordinate descent for learning with trace-norm regularization. In Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS).
12. Farhadi, A.; Endres, I.; Hoiem, D.; and Forsyth, D. 2009. Describing objects by their attributes. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
13. Fuernkranz, J.; Huellermeier, E.; Mencia, E.; and Brinker, K. 2008. Multilabel classification via calibrated label ranking. Machine Learning 73 (2).
14. Gantner, Z.; Drumond, L.; Freudenthaler, C.; Rendle, S.; and Schmidt-Thieme, L. 2010. Learning attribute-to-feature mappings for cold-start recommendations. In Proceedings of the IEEE Conference on Data Mining (ICDM), 176-185.
15. Globerson, A.; Chechik, G.; Pereira, F.; and Tishby, N. 2007. Euclidean embedding of co-occurrence data. Journal of Machine Learning Research 8:2265-2295.
16. Grave, E.; Obozinski, G.; and Bach, F. 2011. Trace lasso: a trace norm regularization for correlated designs. In Advances in Neural Information Processing Systems (NIPS).
17. Guo, Y., and Schuurmans, D. 2011. Adaptive large margin training for multilabel classification. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI).
18. Hinton, G., and Paccanaro, A. 2002. Learning hierarchical structures with linear relational embedding. In Proceedings of Advances in Neural Information Processing Systems (NIPS).
19. Jaschke, R.; Marinho, L. B.; Hotho, A.; Schmidt-Thieme, L.; and Stumme, G. 2008. Tag recommendations in social bookmarking systems. AI Communications 21(4):231-247.
20. Jenatton, R.; Roux, N. L.; Bordes, A.; and Obozinski, G. 2012. A latent factor model for highly multi-relational data. In Proceedings of Advances in Neural Information Processing Systems (NIPS).
21. Ji, S., and Ye, J. 2009. An accelerated gradient method for trace norm minimization. In Proceedings of the International Conference on Machine Learning (ICML).
22. Joachims, T. 2002. Optimizing search engines using clickthrough data. In Proceedings of the ACM SIGKDD Conferece on Knowledge Discovery and Data Mining.
23. Joachims, T 2005. A support vector method for multivariate performance measures. In Proceedings of the International Conference on Machine Learning (ICML).
24. Menon, A., and Elkan, C. 2011. Link prediction via matrix factorization. In European Conference on Machine Learning (ECML).
25. Newman, M. 2010. Networks: An Introduction. Oxford.
26. Nickel, M.; Tresp, V.; and Kriegel, H. 2011. A three-way model for collective learning on multi-relational data. In Proceedings of the International Conference on Machine Learning (ICML).
27. Recht, B.; Fazel, M.; and Parrilo, P. 2010. Guaranteed minimumrank solutions of linear matrix equations via nuclear norm minimization. SIAM Review 52:471-501.
28. Rendle, S., and Schmidt-Thieme, L. 2009. Factor models for tag recommendation in bibsonomy. In ECMUPKDD Discovery Challenge.
29. Rendle, S., and Schmidt-Thieme, L. 2010. Pairwise interaction tensor factorization for personalized tag recommendation. In International Conference on Web Search and Data Mining, 81-90.
30. Rendle, S.; Freudenthaler, C.; Gantner, Z.; and Schmidt-Thieme, L. 2009. BPR: Bayesian personalized ranking from implicit feedback. In Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI).
31. Sebastiani, F. 2002. Machine learning in automated text categorization. ACM Computing Surveys 34(1):1-7.
32. Socher, R.; Chen, D.; Manning, C.; and Ng, A. 2013. Reasoning with neural tensor networks for knowledge base completion. In Advances in Neural Infonnation Processing Systems (NIPS).
33. Srebro, N.; Rennie, J.; and Jaakkola, T. 2004. Large-margin matrix factorization. In Proceedings of Advances in Neural Information Processing Systems (NIPS).
34. Sutskever, I., and Hinton, G. 2008. Using matrices to model symbolic relationship. In Proceedings of Advances in Neural Information Processing Systems (NIPS).
35. Tsochantaridis, I.; Hofmann, T.; Joachims, T.; and Altun, Y. 2005. Large margin methods for structured and interdependent output variables. Journal of Machine Learning Research 6:1453-1484.
36. Tsoumakas, G.; Katakis, I.; and Vlahavas, I. 2009. Mining multilabel data. In Data Mining and Knowledge Discovery Handbook, 2nd edition. Springer.
37. Usunier, N.; Buffoni, D.; and Gallinari, P. 2009. Ranking with ordered weighted pairwise classification. In Proceedings of the International Conference on Machine Learning (ICML).
38. Weinberger, K., and Saul, L. 2009. Distance metric learning for large margin nearest neighbor classification. Journal of Machine Learning Research 10:207-244.
39. Weston, J.; Bengio, S.; and Usunier, N. 2010. Large scale image annotation: Learning to rank with joint word-image embeddings. Machine Learning 81(1):21-35.
40. Weston, J.; Bengio, S.; and Usunier, N. 2011. WSABIE: Scaling up to large vocabulary image annotation. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI).
41. Zhang, X.; Yu, Y.; and Schuurmans, D. 2012. Accelerated training for matrix-norm regularization: A boosting approach. In Advances in Neural Information Processing Systems (NIPS).