Heterogeneous multi-task semantic feature learning for classification

International Conference on Information and Knowledge Management, Proceedings

Volumn 19-23-Oct-2015, Issue , 2015, Pages 1847-1850

  Jin, Xin ^a,b   Zhuang, Fuzhen ^a   Pan, Sinno Jialin ^c   Du, Changying ^a   Luo, Ping ^a   He, Qing ^a  

^a INSTITUTE OF COMPUTING TECHNOLOGY   (China)

^b HUAWEI TECHNOLOGIES CO LTD   (China)

^c NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

Author keywords

[No Author keywords available]

Indexed keywords

FACTORIZATION; KNOWLEDGE MANAGEMENT; LINEARIZATION; MATRIX ALGEBRA; SEMANTICS;

FEATURE REPRESENTATION; GENERALIZATION PERFORMANCE; HETEROGENEOUS FEATURES; MULTI-VIEW LEARNING; MULTITASK LEARNING; NONNEGATIVE MATRIX FACTORIZATION; REAL-WORLD PROBLEM; SEMANTIC FEATURES;

PROBLEM SOLVING;

EID: 84958243165     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2806416.2806644     Document Type: Conference Paper

References (25)

1. R. K. Ando and T. Zhang. A framework for learning predictive structures from multiple tasks and unlabeled data. JMLR, 6:01, 2005.
2. A. Argyriou, T. Evgeniou, and M. Pontil. Multi-task feature learning. In NIPS, pages 41-48, Vancouver, BC, Canada, 2007.
3. S. Bickel. Ecml-pkdd discovery challenge 2006 overview. In ECML-PKDD Discovery Challenge Workshop, pages 1-9, 2006.
4. J. Blitzer, M. Dredze, and F. Pereira. Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification. In ACL, pages 440-447, Prague, Czech republic, 2007.
5. R. Caruana. Multitask learning. Machine learning, 28(1):41-75, 1997.
6. J. Chen, L. Tang, J. Liu, and J. Ye. A convex formulation for learning shared structures from multiple tasks. In ICML, pages 137-144, Montreal, QC, Canada, 2009.
7. J. Chen, J. Zhou, and J. Ye. Integrating low-rank and group-sparse structures for robust multi-task learning. In ACM SIGKDD, pages 42-50, San Diego, CA, United states, 2011.
8. W. Dai, Y. Chen, G.-R. Xue, Q. Yang, and Y. Yu. Translated learning: Transfer learning across different feature spaces. In NIPS, pages 353-360, 2008.
9. C. Ding, T. Li, W. Peng, and H. Park. Orthogonal nonnegative matrix t-factorizations for clustering. In ACM SIGKDD, pages 126-135. ACM, 2006.
10. L. Duan, D. Xu, and I. W. Tsang. Learning with augmented features for heterogeneous domain adaptation. In ICML, 2012.
11. T. Evgeniou and M. Pontil. Regularized multi-task learning. In ACM SIGKDD, pages 109-117, Seattle, WA, United states, 2004.
12. P. Gong, J. Ye, and C. Zhang. Robust multi-task feature learning. In ACM SIGKDD, pages 895-903. ACM, 2012.
13. J. He and R. Lawrence. A graph-based framework for multi-task multi-view learning. In ICML, pages 25-32, Bellevue, WA, United states, 2011.
14. J. He, Y. Liu, and Q. Yang. Linking heterogeneous input spaces with pivots for multi-task learning. In SDM, 2014.
15. A. Jalali, P. Ravikumar, S. Sanghavi, and C. Ruan. A dirty model for multi-task learning. In NIPS, Vancouver, BC, Canada, 2010.
16. X. Jin, F. Zhuang, S. Wang, Q. He, and Z. Shi. Shared structure learning for multiple tasks with multiple views. In ECML PKDD, volume 8189 LNAI, pages 353-368, Prague, Czech republic, 2013.
17. X. Jin, F. Zhuang, H. Xiong, C. Du, P. Luo, and Q. He. Multi-task multi-view learning for heterogeneous tasks. In CIKM, pages 441-450, New York, NY, USA, 2014. ACM.
18. T. Joachims. Transductive inference for text classification using support vector machines. In ICML, pages 200-209, San Francisco, CA, USA, 1999.
19. Z. Kang, K. Grauman, and F. Sha. Learning with whom to share in multi-task feature learning. In ICML, pages 521-528, 2011.
20. G.-J. Qi, C. Aggarwal, and T. Huang. Towards semantic knowledge propagation from text corpus to web images. In WWW, pages 297-306. ACM, 2011.
21. G. Skolidis and G. Sanguinetti. Bayesian multitask classification with gaussian process priors. IEEE Transactions on Neural Networks, 22(12):2011-2021, 2011.
22. S. Yu, V. Tresp, and K. Yu. Robust multi-task learning with t-processes. In ICML, volume 227, pages 1103-1110, Corvalis, OR, United states, 2007.
23. J. Zhang and J. Huan. Inductive multi-task learning with multiple view data. In ACM SIGKDD, pages 543-551, Beijing, China, 2012.
24. Y. Zhang and D.-Y. Yeung. Multi-task learning in heterogeneous feature spaces. In AAAI, volume 1, pages 574-579, San Francisco, CA, United states, 2011.
25. Y. Zhu, Y. Chen, Z. Lu, S. J. Pan, G.-R. Xue, Y. Yu, and Q. Yang. Heterogeneous transfer learning for image classification. In AAAI, 2011.