Abstract
In regression tasks for static data, existing methods often assume that they were generated from an identical and independent distribution (i.i.d.). However, violation can be found when input samples may form groups, each affected by a certain different domain. In this case, style consistency exists within a same group, leading to degraded performance when conventional machine learning models were applied due to the violation of the i.i.d. assumption. In this paper, we propose one novel regression model named Field Support Vector Regression (F-SVR) without i.i.d. assumption. Specifically, we perform a style normalization transformation learning and the regression model learning simultaneously. An alternative optimization with final convergence guaranteed is designed, as well as a transductive learning algorithm, enabling extension on unseen styles during the testing phase. Experiments are conducted on two synthetic as well as two real benchmark data sets. Results show that the proposed F-SVR significantly outperforms many other state-of-the-art regression models in all the used data sets.
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Notes
- 1.
We only give the linear formulation for simplicity, however, it can be easily extended to a kernelized version, enabling the nonlinear F-SVR.
References
Argyriou, A., Evgeniou, T., Pontil, M.: Convex multi-task feature learning. Mach. Learn. 73(3), 243–272 (2008)
Evgeniou, T., Pontil, M.: Regularized multi-task learning. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 109–117. ACM (2004)
Kang, Z., Grauman, K., Sha, F.: Learning with whom to share in multi-task feature learning. In: Proceedings of the 28th International Conference on Machine Learning (ICML), pp. 521–528 (2011)
Kumar, A., Daume III, H.: Learning task grouping and overlap in multi-task learning. arXiv preprint arXiv:1206.6417 (2012)
Lenk, P.J., DeSarbo, W.S., Green, P.E., Young, M.R.: Hierarchical bayes conjoint analysis: recovery of partworth heterogeneity from reduced experimental designs. Mark. Sci. 15(2), 173–191 (1996)
Smola, A.J., Schölkopf, B.: A tutorial on support vector regression. Stat. Comput. 14(3), 199–222 (2004)
Zhang, X.Y., Huang, K., Liu, C.L.: Pattern field classification with style normalized transformation. In: International Joint Conference on Artificial Intelligence (IJCAI), pp. 1621–1626. AAAI Press (2011)
Acknowledgement
The paper was partially supported by National Natural Science Foundation of China (NSFC) under grant no. 61473236, Natural Science Fund for Colleges and Universities in Jiangsu Province under grant no. 17KJD520010, and Suzhou Science and Technology Programme under grant no. SYG201712, SZS201613.
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Jiang, H., Huang, K., Zhang, R. (2017). Field Support Vector Regression. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10634. Springer, Cham. https://doi.org/10.1007/978-3-319-70087-8_72
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DOI: https://doi.org/10.1007/978-3-319-70087-8_72
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