For solving the clustering problem of transfer learning, a new algorithm called Transfer Affinity Propagation clustering algorithm is proposed based on Kullback-Leiber distance (TAP_KL). Based on the probabilistic framework, a new interpretation of the objective function of Affinity Propagation (AP) clustering algorithm is proposed. By leveraging Kullback-Leiber distance which is usually used in information theory, TAP_KL measures the similarity relationship between source data and target data. Moreover, TAP_KL algorithm can embed the similarity relationship to the calculation of similarity matrix of target data. Thus, the optimization framework of AP can be directly used to optimize the new target function of TAP_KL. In this case, TAP_KL builds a simple algorithm framework to solve the transfer clustering problem, in which the algorithm just needs to modify the similarity matrix to solve the transfer clustering problem. The experimental results based on both 4 datasets show the effectiveness of the proposed algorithm TAP_KL.
毕安琪,王士同. 基于Kullback-Leiber距离的迁移仿射聚类算法[J]. 电子与信息学报, 2016, 38(8): 2076-2084.
BI Anqi, WANG Shitong. Transfer Affinity Propagation Clustering Algorithm Based on Kullback-Leiber Distance. JEIT, 2016, 38(8): 2076-2084.
LONG M, WANG J, DING G, et al. Adaptation regularization: A general framework for transfer learning[J]. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(5): 1076-1089. doi: 10.1109/TKDE.2013.111.
BI Anqi and WANG Shitong. Transfer classification learning based on combination of both SVC and SVR’s constraints[J]. Control and Decision, 2014, 29(6): 1021-1026. doi: 10.13195/j. kzyjc.2013.0520.
[3]
PATRICIA N and CAPUTO B. Learning to learn, from transfer learning to domain adaptation: A unifying perspective[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA, 2014: 1442-1449. doi: 10.1109/CVPR.2014.187.
[4]
XU Z J and SUN S L. Multi-source transfer learning with multi-view adaboost[J]. Neural Information Processing, 2012, 7665: 332-339. doi: 10.1007/978-3-642-34487-9_41.
[5]
PAN S J L, KWOK J T, and YANG Q. Transfer learning via dimensionality reduction[C]. Proceedings of the 23rd International Conference on Artificial Intelligence, CA, USA: 2008: 677-682.
[6]
PAN S J L, NI X C, SUN J T, et al. Cross domain sentiment classification via spectral feature alignment[C]. Proceedings of the 19th International Conference on World Wide Web (WWW-10). New York, USA, 2010, 751-760. doi: 10. 1145/1772690.1772767.
JIANG Yizhang, DENG Zhaohong, and WANG Shitong. Mamdani-Larsen type transfer learning fuzzy system[J]. Acta Automatica Sinica, 2012, 38(9): 1393-1409. doi: 10. 3724/SP.J.1004.2012.01393.
[8]
JIANG W H and CHUNG F L. Transfer spectral clustering [C]. Proceedings of the 2012 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Berlin, Heidelberg, 2012: 789-803. doi: 10.1007/978-3-642-33486-3_50.
[9]
LI M J, NG M K, CHEUNG Y M, et al. Agglomerative fuzzy K-means clustering algorithm with selection of number of clusters[J]. IEEE Transactions on Knowledge and Data Engineering, 2008, 20(11): 1519-1534. doi: 10.1109/TKDE. 2008.88.
[10]
KRISHMA K and MURTY M N. Genetic K-means algorithm[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B, 1999, 29(3): 433-439. doi: 10.1109/3477. 764879.
[11]
ERSAHIN K, CUMMING I G, and WARD R K. Segmentation and classification of Polari metric SAR data using spectral graph partitioning[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(1): 164-167. doi: 10.1109/TGRS.2009.2024303.
[12]
LAUER F and SCHNORR C. Spectral clustering of linear subspaces for motion segmentation[C]. Proceedings of the 12th IEEE International Conference of Computer Vision, Kyoto, Japan, 2009: 678-685. doi: 10.1109/ICCV. 2009. 5459173.
[13]
FREY B J and DUECK D. Clustering by passing messages between data points[J]. Science, 2007, 315(5814): 972-976.
XIAO Yu and YU Jian. Semi-supervised clustering based on affinity propagation algorithm[J]. Journal of Software, 2008, 19(11): 2803-2813. doi: 10.3724/SP.J.1001.2008.02803.
CHU Yuezhong, XU Bo, and GAO Youtao. Technique of remote sensing image target recognition based on affinity propagation and kernel matching pursuit[J]. Journal of Electronics & Information Technology, 2014, 36(12): 2923-2928. doi: 10.3724/SP.J.1146.2014.00422.
[16]
SUN L and GUO C H. Incremental affinity propagation clustering based on message passing[J]. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(11): 2731-2744. doi: 10.1109/TKDE.2014.2310215.
[17]
SHI X H, GUAN R C, WANG L P, et al. An incremental affinity propagation algorithm and its applications for text clustering[C]. Proceedings International Joint Conference on Neural Networks, Atlanta, GA, USA, 2009: 2914-2919. doi: 10.1109/IJCNN.2009.5178973.
[18]
ZHENG Yun and CHEN Pei. Clustering based on enhanced -expansion move[J]. IEEE Transactions on Knowledge and Data Engineering, 2013, 25(10): 2206-2216. doi: 10.1109/ TKDE.2012.202.
BI Anqi, DONG Aimei, and WANG Shitong. A dynamic data stream clustering algorithm based on probability and exemplar[J]. Journal of Computer Research and Development, 2016, 53(5): 1029-1042.
SUN Lijuan, CHEN Xiaodong, HAN Chong, et al. New fuzzy- clustering algorithm for data stream[J]. Journal of Electronics & Information Technology, 2015, 37(7): 1620-1625. doi: 10.11999/JEIT141415.
[21]
JIANG Y Z, CHUNG F L, WANG S T, et al. Collaborative fuzzy clustering from multiple weighted views[J]. IEEE Transactions on Cybernetics, 2015, 45(4): 688-701. doi: 10. 1109/TCYB.2014.2334595.
[22]
CAI D, HE X F, HAN J W, et al. Orthogonal Laplacian faces for face recognition[J]. IEEE Transactions on Image Processing, 2006, 15(11): 3608-3614. doi: 10.1109/TIP.2006. 881945.
ZHANG Jingxiang, WANG Shitong, DENG Zhaohong, et al. A subspace transfer learning algorithm integrating heterogeneous features[J]. Acta Automatica Sinica, 2014, 40(2): 236-246. doi: 10.3724/SP.J.1004.2014.00236.
[24]
LE C Y, BOTTOU L, BENGIO Y, et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11): 2278-2324. doi: 10.1109/5.726791.
[25]
REN J, SHI X, FAN W, et al. Type independent correction of sample selection bias via structural discovery and re- balancing[C]. Proceedings of the 8th SIAM International Conference on Data Mining, Atlanta, GA, USA, 2008: 565-576. doi: 10.1137/1.9781611972788.52.
