核分布一致局部领域适应学习

陶剑文; 王士同

doi:10.3724/SP.J.1004.2013.01295

核分布一致局部领域适应学习

doi: 10.3724/SP.J.1004.2013.01295

陶剑文^1,2,
王士同¹

1.
江南大学数字媒体学院无锡 214122;
2.
浙江工商职业技术学院电子与信息工程学院宁波 315012

基金项目:

国家自然科学基金(60975027, 60903100);教育部人文社会科学研究规划基金(13YJAZH084);浙江省自然科学基金(LY13F020011)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, 机器学习.E-mail: wxwangst@yahoo.com.cn

计量
- 文章访问数: 1815
- HTML全文浏览量: 68
- PDF下载量: 1775
- 被引次数: 0
出版历程
- 收稿日期: 2011-10-28
- 修回日期: 2012-03-19
- 刊出日期: 2013-08-20

Kernel Distribution Consistency Based Local Domain Adaptation Learning

TAO Jian-Wen^1,2,
WANG Shi-Tong¹

1.
School of Digital Media, Southern Yangtze University, Wuxi 214122;
2.
School of Electronics and Information Engineering, Zhejiang Business Technology Institute, Ningbo 315012

Funds:

Supported by National Natural Science Foundation of China (60975027, 60903100), Humanities and Social Sciences Research Fund of Ministry of Education (13YJAZH084), and Natural Science Foundation of Zhejiang Province (LY13F020011)

摘要

摘要: 针对领域适应学习(Domain adaptation learning, DAL)问题,提出一种核分布一致局部领域适应学习机(Kernel distribution consistency based local domain adaptation classifier, KDC-LDAC),在某个通用再生核Hilbert空间(Universally reproduced kernel Hilbert space, URKHS),基于结构风险最小化模型, KDC-LDAC首先学习一个核分布一致正则化支持向量机(Support vector machine, SVM),对目标数据进行初始划分; 然后,基于核局部学习思想,对目标数据类别信息进行局部回归重构; 最后,利用学习获得的类别信息,在目标领域训练学习一个适于目标判别的分类器.人造和实际数据集实验结果显示,所提方法具有优化或可比较的领域适应学习性能.
- 领域适应学习 /
- 核分布一致 /
- 局部学习 /
- 模式分类 /
- 最大平均差
Abstract: In allusion to domain adaptation learning (DAL) problems, this paper proposes a novel so-called kernel distribution consistency based local domain adaptation classifier (KDC-LDAC). Firstly, in some universally reproduced kernel Hilbert space (URKHS), the KDC-LDAC trains a kernel distribution consistency regularized domain adaptation support vector machine (SVM) based on the structure risk minimization model, which extends the formulation of classical SVMs to the domain adaptation learning schema. And secondly, according to the idea of local learning, the proposed method predicts the label of each data point in target domain based on its neighbors and their labels in the URKHS. The last but not least, the KDC-LDACs learning a discriminant function to classify the unseen data in target domain with training data well predicted in the local learning procedure. Experimental results on artificial and real world problems show the advantages or comparable effectiveness of the proposed approach compared to related approaches.
- Domain adaptation learning (DAL) /
- kernel distribution consistency (KDC) /
- local learning /
- pattern classification /
- maximum mean discrepancy (MMD)

HTML全文

参考文献(29)

[1]	Pan S J, Tsang I W, Kwok J T, Yang Q. Domain adaptation via transfer component analysis. IEEE Transactions on Neural Networks, 2011, 22(2): 199-210
[2]	Xiang E W, Cao B, Hu D H, Yang Q. Bridging domains using world wide knowledge for transfer learning. IEEE Transactions on Knowledge and Data Engineering, 2010, 22(6): 770-783
[3]	Joachims T. Transductive inference for text classification using support vector machines. In: Proceedings of the 16th International Conference on Machine Learning. San Francisco, CA, USA: Morgan Kaufmann Publishers, 1999. 200-209
[4]	Ozawa S, Roy A, Roussinov D. A multitask learning model for online pattern recognition. IEEE Transactions on Neural Networks, 2009, 20(3): 430-445
[5]	Pan S J, Yang Q. A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 2010, 22(10): 1345-1359
[6]	Bruzzone L, Marconcini M. Domain adaptation problems: a DASVM classification technique and a circular validation strategy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(5): 770-787
[7]	Quanz B, Huan J. Large margin transductive transfer learning. In: Proceedings of the 18th ACM Conference on Information and Knowledge Management (CIKM). New York, NY, USA: ACM, 2009. 1327-1336
[8]	Ben-David S, Blitzer J, Crammer K, Pereira F. Analysis of representations for domain adaptation. In: Proceedings of the 2006 Conference on Advances in Neural Information Processing Systems 19. Cambridge, MA: MIT Press, 2007. 137-144
[9]	Ling X, Dai W Y, Xue G R, Yang Q, Yu Y. Spectral domain-transfer learning. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2008. 488-496
[10]	Dai W Y, Xue G R, Yang Q, Yu Y. Co-clustering based classification for out-of-domain documents. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Jose, California, USA: ACM, 2007. 210-219
[11]	Blitzer J, McDonald R, Pereira F. Domain adaptation with structural correspondence learning. In: Proceedings of the 2006 Conference on Empirical Methods Natural Language Processing. Stroudsburg, PA, USA: Association for Computational Linguistics, 2006. 120-128
[12]	Blitzer J, Dredze M, Pereira F. Biographies, bollywood, boom-boxes and blenders: domain adaptation for sentiment classification. In: Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics (ACL'07). 2007. 440-447
[13]	Sriperumbudur B K, Gretton A, Fukumizu K, Schölkopf B, Lanckriet G R G. Hilbert space embeddings and metrics on probability measures. Journal of Machine Learning Research, 2010, 11(3): 1517-1561
[14]	Gretton A, Harchaoui Z, Fukumizu K, Sriperumbudur B K. A fast, consistent kernel two-sample test. In: Proceedings of Advances in Neural Information Processing Systems 22, the 23rd Annual Conference on Neural Information Processing Systems (NIPS 2009). Red Hook, NY: MIT Press, 2010. 673-681
[15]	Vapnik V N. Statistical Learning Theory. New York: John Wiley and Sons, 1998
[16]	Zeng H, Cheung Y M. Feature selection and kernel learning for local learning-based clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(8): 1532-1547
[17]	Wu M R, Schölkopf B. Transductive classification via local learning regularization. In: Proceedings of the 11th International Conference Artificial Intelligence and Statistics. Cambridge, MA: MIT Press, 2007. 628-635
[18]	Hofmann T, Schölkopf B, Smola A J. Kernel methods in machine learning. Annals of Statistics, 2008, 36(3): 1171-1220
[19]	Sriperumbadur B K, Fukumizu K, Gretton A, Lanckriet G, Schoelkopf B. Kernel choice and classifiability for RKHS embeddings of probability distributions. In: Proceedings of Advances in Neural Information Processing Systems 22, the 23rd Annual Conference on Neural Information Processing Systems (NIPS 2009). Red Hook, NY: MIT Press, 2010. 1750-1758
[20]	Smola A J, Gretton A, Song L, Schölkopf B. A Hilbert space embedding for distributions. In: Proceedings of the 18th International Conference on Algorithmic Learning Theory. Sendai, Japan: Springer-Verlag, 2007. 13-31
[21]	Schölkopf B, Herbrich R, Smola A J. A generalized representer theorem. In: Proceedings of the 14th Annual Conference on Computational Learning Theory COLT'2001. Berlin Heidelberg: Springer Press, 2001. 416-426
[22]	Wu Y C, Liu Y F. Robust truncated hinge loss support vector machines. Journal of the American Statistical Association, 2007, 102(479): 974-983
[23]	Tao Jian-Wen, Wang Shi-Tong. Locality-preserved maximum information variance v-support vector machine. Acta Automatica Sinica, 2012, 38(1): 79-108 (陶剑文, 王士同. 局部保留最大信息差v-支持向量机. 自动化学报, 2012, 38(1): 79-108)
[24]	Belkin M, Niyogi P, Sindhwani V. Manifold regularization: A geometric framework for learning from labeled and unlabeled examples. Journal of Machine Learning Research, 2006, 7: 2399-2434
[25]	Cai D, He X F, Han J W, Zhang H J. Orthogonal Laplacianfaces for face recognition. IEEE Transactions on Image Processing, 2006, 15(11): 3608-3614
[26]	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
[27]	Huang J Y, Smola A J, Gretton A, Borgwardt K M, Schölkopf B. Correcting sample selection bias by unlabeled data. In: Proceedings of the 20th Annual Conference on Neural Information Processing Systems. Cambridge, MA: MIT Press, 2006. 601-608
[28]	Jiang W, Zavesky E, Chang S F, Loui A. Cross-domain learning methods for high-level visual concept classification. In: Proceedings of the 15th IEEE International Conference on Image Processing. San Diego, CA: IEEE, 2008. 161-164
[29]	Xu D, Chang S F. Video event recognition using kernel methods with multilevel temporal alignment. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008, 30(11): 1985-1997