Feature Joint Probability Distribution and Instance Based Transfer Learning Algorithm
ZHAO Peng, WU Guoqin, LIU Huiting, YAO Sheng
Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education,Anhui University, Hefei 230039 School of Computer Science and Technology, Anhui University, Hefei 230601
Abstract：Aiming at the poor generalization ability of only matching marginal probability distribution to reduce the domain difference, a feature joint probability distribution and instance based transfer learning algorithm (FJPD-ITLA) is proposed. The instances are represented with the kernel principal component analysis in subspace. In this subspace, the maximum mean discrepancy is expanded to jointly match the marginal and conditional probability distribution. Thus, the difference between the source domain and target domain is reduced. Meanwhile, the L2,1-norm constraint is utilized to choose relevant instances in the source domain, and the generalization ability of the model obtained by transfer learning is improved further. Experimental results on the digital and object recognition datasets demonstrate the validity and efficiency of the proposed algorithm.
赵鹏，吴国琴，刘慧婷，姚晟. 基于特征联合概率分布和实例的迁移学习算法*[J]. 模式识别与人工智能, 2016, 29(8): 717-725.
ZHAO Peng, WU Guoqin, LIU Huiting, YAO Sheng. Feature Joint Probability Distribution and Instance Based Transfer Learning Algorithm. , 2016, 29(8): 717-725.
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2016, Vol. 29 
