Transfer Affinity Propagation Clustering Algorithm Based on Kullback-Leiber Distance
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摘要: 针对迁移聚类问题,该文提出一种新的基于Kullback-Leiber距离的迁移仿射聚类算法(TAP_KL)。该算法从概率角度重新解释AP算法的目标函数,并借助于信息论中最常见的一种距离度量,即Kullback-Leiber距离,测量源域与目标域代表点的相似性。另外,通过详细分析TAP_KL算法与AP算法的目标函数,得出一个重要结论,即可以将源域与目标域的相似性嵌入到目标域数据集相似性矩阵的计算中,从而直接利用AP算法的优化算法优化TAP_KL算法的目标函数,解决基于代表点的迁移聚类问题。最后,通过基于4个数据集的仿真实验,进一步验证了TAP_KL算法在解决迁移聚类问题时的有效性。Abstract: 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.
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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. 毕安琪, 王士同. 基于SVC和SVR约束组合的迁移学习分类算法[J]. 控制与决策, 2014, 29(6): 1021-1026. doi: 10.13195 /j.kzyjc.2013.0520. BI Anqi and WANG Shitong. Transfer classification learning based on combination of both SVC and SVRs constraints[J]. Control and Decision, 2014, 29(6): 1021-1026. doi: 10.13195/j. kzyjc.2013.0520. 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. 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. 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. 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. 蒋亦樟, 邓赵红, 王士同. ML型迁移学习模糊系统[J]. 自动化学报, 2012, 38(9): 1393-1409. doi: 10.3724/SP.J.1004.2012. 01393. 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. 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. 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. 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. 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. 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. FREY B J and DUECK D. Clustering by passing messages between data points[J]. Science, 2007, 315(5814): 972-976. 肖宇, 于剑. 基于近邻传播算法的半监督聚类[J]. 软件学报, 2008, 19(11): 2803-2813. doi: 10.3724/SP.J.1001.2008.02803. 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. 储岳中, 徐波, 高有涛. 基于近邻传播聚类与核匹配追踪的遥感图像目标识别方法[J]. 电子与信息学报, 2014, 36(12): 2923-2928. doi: 10.3724/SP.J.1146.2014.00422. 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. 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. 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. 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. 毕安琪, 董爱美, 王士同. 基于概率和代表点的数据流动态聚类算法[J]. 计算机研究与发展, 2016, 53(5): 1029-1042. 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. 孙力娟, 陈小东, 韩崇, 等. 一种新的数据流模糊聚类方法[J]. 电子与信息学报, 2015, 37(7): 1620-1625. doi: 10.11999/ JEIT141415. 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. 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. 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. 张景祥, 王士同, 邓赵红, 等. 融合异构特征的子空间迁移学习算法[J]. 自动化学报, 2014, 40(2): 236-246. doi: 10.3724/SP.J. 1004.2014.00236. 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. 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. 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.
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