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.
Fund:Supported by National Natural Science Foundation of China(No.61602004,61472001), Natural Science Foundation of Anhui Province(No.1508085MF127,1408085MF122), Natural Science Foundation of the Education Department of Anhui Province(No.KJ2016A041), Foundation of Center of Information Support & Assurance Technology(No.ADXXBZ2014-5,ADXXBZ2014-6).
About author:: (ZHAO Peng(Corresponding author), born in 1976, Ph.D., associate professor. Her research interests include intelligent information processing and machine learning.)(WU Guoqin, born in 1990, master student. Her research interests include machine learning and artificial intelligence.)(LIU Huiting, born in 1978, Ph.D., associate professor. Her research interests include machine learning and data mining.)(YAO Sheng, born in 1979, Ph.D., lecturer. Her research interests include data mining.)
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2016, Vol. 29 
