Take a look at the new version of this page: [ beta version ]. Tell us what you think.

ABSTRACT

Transfer learning is the task of leveraging the information from labeled examples in some domains to predict the labels for examples in another domain. It finds abundant practical applications, such as sentiment prediction, image classification and network intrusion detection. In this paper, we propose a graph-based transfer learning framework. It propagates the label information from the source domain to the target domain via the example-feature-example tripartite graph, and puts more emphasis on the labeled examples from the target domain via the example-example bi-partite graph. Our framework is semi-supervised and non-parametric in nature and thus more flexible. We also develop an iterative algorithm so that our framework is scalable to large-scale applications. It enjoys the theoretical property of convergence. Compared with existing transfer learning methods, the proposed framework propagates the label information to both the features irrelevant to the source domain and the unlabeled examples in the target omain via the common features in a principled way. Experimental results on 3 real data sets demonstrate the effectiveness of our algorithm.

REFERENCES

• 1: Kdd cup 99. In http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html, 1999.
• 2: Rie Kubota Ando , Tong Zhang, A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data, The Journal of Machine Learning Research, 6, p.1817-1853, 12/1/2005
• 3: Jonathan Baxter, A Bayesian/Information Theoretic Model of Learning to Learn viaMultiple Task Sampling, Machine Learning, v.28 n.1, p.7-39, July 1997 [doi>10.1023/A:1007327622663]
• 4: J. Blitzer, M. Dredze, and F. Pereira. Biographies, bollywood, boom--boxes and blenders: Domain adaptation for sentiment classification. In ACL, 2007.
• 5: Rich Caruana, Multitask Learning, Machine Learning, v.28 n.1, p.41-75, July 1997 [doi>10.1023/A:1007379606734]
• 6: Wenyuan Dai , Gui-Rong Xue , Qiang Yang , Yong Yu, Co-clustering based classification for out-of-domain documents, Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, August 12-15, 2007, San Jose, California, USA [doi>10.1145/1281192.1281218]
• 7: Wenyuan Dai , Qiang Yang , Gui-Rong Xue , Yong Yu, Boosting for transfer learning, Proceedings of the 24th international conference on Machine learning, p.193-200, June 20-24, 2007, Corvalis, Oregon [doi>10.1145/1273496.1273521]
• 8: Wei Fan , Ian Davidson , Bianca Zadrozny , Philip S. Yu, An Improved Categorization of Classifier's Sensitivity on Sample Selection Bias, Proceedings of the Fifth IEEE International Conference on Data Mining, p.605-608, November 27-30, 2005 [doi>10.1109/ICDM.2005.24]
• 9: Jing Gao , Wei Fan , Jing Jiang , Jiawei Han, Knowledge transfer via multiple model local structure mapping, Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, August 24-27, 2008, Las Vegas, Nevada, USA [doi>10.1145/1401890.1401928]
• 10: Su-In Lee , Vassil Chatalbashev , David Vickrey , Daphne Koller, Learning a meta-level prior for feature relevance from multiple related tasks, Proceedings of the 24th international conference on Machine learning, p.489-496, June 20-24, 2007, Corvalis, Oregon [doi>10.1145/1273496.1273558]
• 11: Xuejun Liao , Ya Xue , Lawrence Carin, Logistic regression with an auxiliary data source, Proceedings of the 22nd international conference on Machine learning, p.505-512, August 07-11, 2005, Bonn, Germany [doi>10.1145/1102351.1102415]
• 12: Q. Liu, X. Liao, and L. Carin. Semi-supervised multitask learning. In NIPS, 2007.
• 13: J. luc Gauvain and C. hui Lee. Maximum a posteriori estimation for multivariate gaussian mixture observations of markov chains. IEEE Transactions on Speech and Audio Processing, 2:291--298, 1994.
• 14: A. M. Martinez. Recognition of partially occluded and/or imprecisely localized faces using a probabilistic approach. In CVPR, pages 1712--1717, 2000.
• 15: B. Pang, L. Lee, and S. Vaithyanathan. Thumbs up? sentiment classification using machine learning techniques. CoRR, cs.CL/0205070, 2002.
• 16: J. Rennie. 20 newsgroups. In http://people.csail.mit.edu/jrennie/20Newsgroups/, 2007.
• 17: Brian Roark , Michiel Bacchiani, Supervised and unsupervised PCFG adaptation to novel domains, Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology, p.126-133, May 27-June 01, 2003, Edmonton, Canada [doi>10.3115/1073445.1073472]
• 18: H. Roger and J. Charles. Matrix Analysis. Cambridge University Press, 1985.
• 19: S. Thrun. Is learning the n-th thing any easier than learning the first? In NIPS, pages 640--646. MIT Press, 1996.
• 20: Haixun Wang , Wei Fan , Philip S. Yu , Jiawei Han, Mining concept-drifting data streams using ensemble classifiers, Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, August 24-27, 2003, Washington, D.C. [doi>10.1145/956750.956778]
• 21: Pengcheng Wu , Thomas G. Dietterich, Improving SVM accuracy by training on auxiliary data sources, Proceedings of the twenty-first international conference on Machine learning, p.110, July 04-08, 2004, Banff, Alberta, Canada [doi>10.1145/1015330.1015436]
• 22: Bianca Zadrozny, Learning and evaluating classifiers under sample selection bias, Proceedings of the twenty-first international conference on Machine learning, p.114, July 04-08, 2004, Banff, Alberta, Canada [doi>10.1145/1015330.1015425]
• 23: Bianca Zadrozny , Charles Elkan, Learning and making decisions when costs and probabilities are both unknown, Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, p.204-213, August 26-29, 2001, San Francisco, California [doi>10.1145/502512.502540]
• 24: J. Zhang, Z. Ghahramani, and Y. Yang. Learning multiple related tasks using latent independent component analysis. In NIPS, 2005.
• 25: D. Zhou, J. Weston, A. Gretton, O. Bousquet, and B. Scholkopf. Ranking on data manifolds. In NIPS, 2003.
• 26: X. Zhu, Z. Ghahramani, and J. Lafferty. Semi-supervised learning using gaussian fields and harmonic functions. In ICML, pages 912--919, 2003.

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.m MISCELLANEOUS

General Terms:
Algorithms, Experimentation

Keywords:
graph-based, transfer learning