|Table of Contents|

Transfer learning Laplacian total margin support vector machine(PDF)


Research Field:
Publishing date:


Transfer learning Laplacian total margin support vector machine
Lu Bing13Zhou Guohua12Gu Xiaoqing3Yin Xinchun2
1.Department of Information Engineering,Changzhou Institute of Industry Technology,Changzhou213164,China; 2.College of Information Engineering,Yangzhou University,Yangzhou 225127,China; 3.School of Information Science and Engineering,Changzhou University,Changzhou 213164,China
transfer learning support vector machine classification semi-supervised total margin maximum mean discrepancy
To further promote the performance of semi-supervised classifier with insufficient labeled and unlabeled samples,a transfer learning Laplacian total margin support vector machine called TL-Lap-TSVM is studied here. The joint projected maximum mean discrepancy is proposed to measure the distribution differences between the different domains from the global and local aspects. The TL-Lap-TSVM is proposed by introducing the idea of transfer learning into the semi-supervised learning framework. This paper realizes the transferring knowledge from the source domain to the target domain and promotes the performance of classifier in the target domain. The experimental results on eight transfer learning datasets show that the TL-Lap-TSVM can deal with the task of classification in the scenario that both labeled and unlabeled data in the target domain are insufficient.


[1] Tang Xin,Guo Fang,Shen Jianbing,et al. Facial landmark detection by semi-supervised deep learning[J]. Neurocomputing,2018,297(5):22-32.
[2]Gan Haitao,Li Zhenhua,Wu Wei,et al. Safety-aware graph-based semi-supervised learning[J]. Expert Systems with Applications,2018,107(1):243-254.
[3]Ni Tongguang,Chung Fulai,Wang Shitong. Support vector machine with manifold regularization and partially labeling privacy protection[J]. Information Sciences,2015,294(2):390-407.
[4]潘俊,王瑞琴,孔繁胜. 基于结构和约束保持的半监督特征选择[J]. 南京理工大学学报,2014,38(4):518-525.
Pan Jun,Wang Ruiqin,Kong Fansheng. Semi-supervised feature selection based on structure and constraints preserving[J]. Journal of Nanjing University of Science and Technology,2014,38(4):518-525.
[5]侯杰,茅耀斌,孙金生. 一种最大化样本可分性半监督Boosting算法[J]. 南京理工大学学报,2014,38(5):675-681.
Hou Jie,Mao Yaobin,Sun Jinsheng. Semi-supervised separability-maximum boosting[J]. Journal of Nanjing University of Science and Technology,2014,38(5):675-681.
[6]Vapnik V. Statistical learning theory[M]. New York,USA:John Wiley and Sons,1998.
[7]Belkin M,Niyogi P,Sindhwani V. Manifold regularization:a geometric framework for learning from labeled and unlabeled examples[J]. Journal of Machine Learning Research,2006,7(3):2399-2434.
[8]陈伟杰,邵元海,李春娜,等. 基于流形正则化的半监督投影双子支持向量机[J]. 模式识别与人工智能,2016,29(2):97-107.
Chen Weijie,Shao Yuanhai,Li Chunna,et al. Semi-supervised projection twin support vector machine via manifold regularization[J]. Pattern Recognition and Artificial Intelligence,2016,29(2):97-107.
[9]皋军,王士同,邓赵红. 基于全局和局部保持的半监督支持向量机[J]. 电子学报,2010,38(7):1626-1633.
Gao Jun,Wang Shitong,Deng Zhaohong. Global and local preserving based semi-supervised support vector machine[J]. Acta Electronica Sinica,2010,38(7):1626-1633.
[10]姚明海,林宣民,王宪保. 基于局部敏感Hash的半监督直推SVM增量学习算法[J]. 浙江工业大学学报,2018,46(2):127-132.
Yao Minghai,Lin Xuanmin,Wang Xianbao. Incremental learning algorithm of TSVM with locality sensitive Hasing[J]. Journal of Zhejiang University of Technology,2018,46(2):127-132.
[11]Pei Huimin,Chen Yanyan,Wu Yankun,et al. Laplacian total margin support vector machine based on within-class scatter[J]. Knowledge-Based Systems,2017,119(10):152-165.
[12]Pan S J,Yang Qiang. A survey on transfer learning[J]. IEEE Transactions on Knowledge and Data Engineering,2010,22(10):1345-1359.
[13]徐萍,吴超,胡峰俊,等. 基于迁移学习的个性化循环神经网络语言模型[J]. 南京理工大学学报,2018,42(4):401-408.
Xu Qing,Wu Chao,Hu Junfeng,et al. Personalized recurrent neural network language model based on transfer learning[J]. Journal of Nanjing University of Science and Technology,2018,42(4):401-408.
[14]Ni Tongguang,Gu Xiaoqing,Wang Jun,et al. Scalable transfer support vector machine with group probabilities[J]. Neurocomputing,2018,273(1):570-582.
[15]Quanz B,Huan J. Large margin transductive transfer learning[C]//Proceedings of the 18th ACM CIKM. New York,USA:ACM,2009:1327-1336.
[16]Long Mingsheng,Wang Jianmin,Ding Guiguang,et al. Adaptation regularization:a general framework for transfer learning[J]. IEEE Transactions on Knowledge and Data Engineering,2014,26(5):1076-1089.
[17]USPS database.[EB/OL]. [2018-01-28]http://www-i6.informatik.rwth-aachen.de/~keysers/usps. htm.
[18]MNIST database.[EB/OL]. [2018-01-28]http://yann.lecun.com/exdb/mnist.
[19]Phan X H,Nguyen M L,Horiguchi S. Learning to classify short and sparse text & web with hidden topics from large-scale data collections[C]//Proceedings of the 17th International Conference on World Wide Web(WWW008). New York,USA:ACM,2008.
[20]Chang C C,Lin C J. LIBSVM:a library for support vector machines[EB/OL]. [2018-01-28]http://www.csie.ntu.edu.tw/~cjlin/libsvm.
[21]Pan S J,Tsang I W,Kwok J T,et al. Domain adaptation via transfer component analysis[J]. IEEE Transactions on Neural Network,2011,22(2):199-210.
[22]倪彤光,王士同. 适用于不确定类标签数据学习的迁移支持向量机[J]. 控制与决策,2014,29(10):1751-1757.
Ni Tongguang,Wang Shitong. Transfer support vector machine for learning from data with uncertain labels[J]. Control and Decision,2014,29(10):1751-1757.
[23]Fernández A,Jesus M J,Herrera F. A hierarchical genetic fuzzy system based on genetic programming for addressing classification with highly imbalanced and borderline data-sets[J]. Knowledge-Based Systems,2013,38(1):85-104.


Last Update: 2020-02-29