TY - GEN
T1 - Graph-based transfer learning
AU - He, Jingrui
AU - Liu, Yan
AU - Lawrence, Richard
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
KW - Graph-based
KW - Transfer learning
UR - http://www.scopus.com/inward/record.url?scp=74549211339&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=74549211339&partnerID=8YFLogxK
U2 - 10.1145/1645953.1646073
DO - 10.1145/1645953.1646073
M3 - Conference contribution
AN - SCOPUS:74549211339
SN - 9781605585123
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 937
EP - 946
BT - ACM 18th International Conference on Information and Knowledge Management, CIKM 2009
T2 - ACM 18th International Conference on Information and Knowledge Management, CIKM 2009
Y2 - 2 November 2009 through 6 November 2009
ER -