共享隐空间迁移SVM

董爱美; 王士同

doi:10.3724/SP.J.1004.2014.02276

共享隐空间迁移SVM

doi: 10.3724/SP.J.1004.2014.02276

董爱美^1,2,
王士同¹

1.
江南大学数字媒体学院无锡 214122;
2.
齐鲁工业大学信息学院济南 250353

基金项目:

国家自然科学基金(61170122, 61202311),江苏省自然科学基金(BK 2012552), 山东省高等学校科技计划项目(J14LN05) 资助

详细信息

作者简介:
王士同江南大学数字媒体学院教授.主要研究方向为人工智能和机器学习.E-mail: wxwangst@yahoo.com.cn

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- 被引次数: 0
出版历程
- 收稿日期: 2013-05-07
- 修回日期: 2014-04-10
- 刊出日期: 2014-10-20

A Shared Latent Subspace Transfer Learning Algorithm Using SVM

DONG Ai-Mei^1,2,
WANG Shi-Tong¹

1.
School of Digital Media, Jiangnan University, Wuxi 214122;
2.
School of Information, Qilu University of Technology, Jinan 250353

Funds:

Supported by National Natural Science Foundation of China (61170122, 61202311), Natural Science Foundation of Jiangsu Province (BK2012552), and the Project of Shandong Province Higher Educational Science and Technology Program (J14LN05)

摘要

摘要: 在机器学习中,迁移学习被证明能有效使用一个领域信息提高另一个领域中受训模型的分类精度. 迁移学习总是假设相关领域间共享某些隐含因素,但在当前的迁移学习方法中,该部分隐含因素依然未得到充分探讨.本研究引入低维共享隐空间的迁移学习方法,基于经典支持向量机(Support vector machine, SVM)分类模型得到融入共享隐空间的迁移支持向量机,该模型较以往相关方法能更好地利用隐空间这一有效信息,从而提高所得分类器的泛化性能.相关实验结果亦验证了所提方法的有效性.
- 迁移学习 /
- 大间隔分类器 /
- 隐空间 /
- 支持向量机
Abstract: In machine learning, transfer learning is proved to be able to efficiently use the information of one domain to enhance the classification accuracy in another domain. Transfer learning always assumes that the related domains share some correlated latent factors. But the existing transfer learning methods cannot make full use of these latent factors. So a low dimensionality shared latent subspace transfer learning method is proposed. Specifically, a shared latent subspace transfer support vector machine model is proposed in contrast to the classical support vector machine (SVM) model. Compared with traditional transfer learning methods, the proposed method can make better use of the latent subspace, therefore it can enhance the transfer classification performance. Experimental results confirm the efficiency of the proposed method.
- Transfer learning /
- large margin classifier /
- latent space /
- support vector machine (SVM)

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参考文献(27)

[1]	Evgeniou T, Micchelli C A, Pontil M. Learning multiple tasks with kernel methods. Journal of Machine Learning Research, 2005, 6(4): 615-637
[2]	[2] Duan L X, Tsang I W, Xu D. Domains transfer multiple kernel learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(3): 465-479
[3]	[3] Tu W T, Sun S L. A subject transfer framework for egg classification. Neurocomputing, 2012, 82: 109-116
[4]	[4] Pan S J, Yang Q. A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 2010, 22(10): 1345-1359
[5]	[5] Ando R K, Zhang T. A framework for learning predictive structures from multiple tasks and unlabeled data. Journal Machine Learning Research, 2005, 6: 1817-1853
[6]	[6] Zheng V W, Pan J L, Yang Q, Pan J F. Transferring multi-device localization models using latent multi-task learning. In: Proceedings of the 23th International Conference on Artificial Intelligence. Chicago, USA: ACM, 2008. 1427-1432
[7]	[7] Pan S J, Kwok J T, Yang Q. Transfer learning via dimensionality reduction. In: Proceedings of the 23th International Conference on Artificial Intelligence. Chicago, USA: ACM 2008. 677-682
[8]	[8] Si S, Tao D C, Geng B. Bregman divergence-based regularization for transfer subspace learning. IEEE Transactions on Knowledge and Data Engineering, 2010, 22(7): 929-942
[9]	[9] Shao M, Castillo C, Gu Z H, Fu Y. Low-rank transfer subspace learning. In: Proceedings of the 12th International Conference on Data Mining. Brussels, Belgium: IEEE 2012. 1104-1109
[10]	Yang S Z, Lin M, Hou C P, Zhang C S, Wu Y. A general framework for transfer sparse subspace learning. Neural Computing and Applications, 2012, 21(7): 1801-1817
[11]	Gupta S K, Phung D, Adams B, Adams B, Venkatesh S. Regularized nonnegative shared subspace learning. Data mining and knowledge discovery, 2013, 26(1): 57-97
[12]	Vapnik V. Statistical Learning Theory. New Jersey: Wiley-Interscience Press, 1998.
[13]	Domeniconi C, Gunopulos D, Ma S, Yan B J, Al-Razgan M, Papadopoulos D. Locally adaptive metrics for custering high dimensional data. Data Mining and Knowledge Discovery, 2007, 14(1): 63-97
[14]	Wu K L, Yu J, Yang M S. A novel fuzzy clustering algorithm based on a fuzzy scatter matrix with optimality tests. Pattern Recognition Letters, 2005, 26(5): 639-652
[15]	Deng Z H, Choi K S, Chung F L, Wang S T. Enhanced soft subspace clustering integrating within-cluster and between-cluster information. Pattern Recognition, 2010, 43(3): 767- 781
[16]	Yu J, Cheng Q S, Huang H K. Analysis of the weighting exponent in the FCM. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 2004, 34(1): 634 -639
[17]	Yang S, Yan S C, Zhang C, Tang X O. Bilinear analysis for kernel selection and nonlinear feature extraction. IEEE Transactions on Neural Networks, 2007, 18(5): 1442-1452
[18]	Jiang Yi-Zhang, Deng Zhao-Hong, Wang Shi-Tong. Mamdani-Larsen type transfer learning fuzzy system. Acta Automatica Sinica, 2012, 38(9): 1393-1409(蒋亦樟, 邓赵红, 王士同. ML型迁移学习模糊系统. 自动化学报, 2012, 38(9): 1393-1409)
[19]	Golub G H, Van Loan C F. Matrix Computations (3rd Edition). Baltimore: The Johns Hopkins University Press, 1996.
[20]	Gao J, Fan W, Jiang J, Han J W. Knowledge transfer via multiple model local structure mapping. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: ACM, 2008. 283-291
[21]	Wu P, Dietterich T G. Improving SVM accuracy by training on auxiliary data sources. In: Proceedings of the 21st International Conference on Machine Learning. New York, USA: ACM, 2004. 110-117
[22]	Quanz B, Huan J. Large margin transductive transfer learning. In: Proceedings of the 18th ACM Conference on Information and Knowledge Management. New York, USA: ACM, 2009. 1327-1336
[23]	Ji S W, Tang L, Yu S P, Ye J P. A shared-subspace learning framework for multi-label classification. ACM Transactions on Knowledge Discovery From Data, 2010, 4(2), Article No.8, DOI: 10.1145/1754428.1754431
[24]	Tao Jian-Wen, Wang Shi-Tong. Kernel distribution consistency based local domain adaptation learning. Acta Automatica Sinica, 2013, 39(8): 1295-1309 (陶剑文, 王士同. 核分布一致局部领域适应学习. 自动化学报, 2013, 39(8): 1295-1309)
[25]	Dai W Y, Yang Q, Xue G R, Yu Y. Boosting for transfer learning. In: Proceedings of the 24th International Conference on Machine Learning. New York, USA: ACM, 2007. 193-200
[26]	Gu Xin, Wang Shi-Tong, Xu Min. A new cross-multidomain classification algorithm and its fast version for large datasets. Acta Automatica Sinica, 2014, 40(3): 531-547(顾鑫, 王士同, 许敏. 基于多源的跨领域数据分类快速新算法. 自动化学报, 2014, 40(3): 531-547)
[27]	Tao Jian-Wen, Wang Shi-Tong. Domain adaptation kernel support vector machine. Acta Automatica Sinica, 2012, 38(5): 797-811(陶剑文, 王士同. 领域适应核支持向量机. 自动化学报, 2012, 38(5): 797-811)