模式识别与人工智能
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  2020, Vol. 33 Issue (6): 488-495    DOI: 10.16451/j.cnki.issn1003-6059.202006002
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Multi-graph Kernel Based Transfer Learning Method
JIANG You1, ZHANG Daoqiang1, ZHANG Junyi1
1. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106

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Abstract  Labeling medical data is costly and there are differences in the distributions of the neuroimaging data provided by different research centers. Therefore, it is nearly impossible to improve the diagnosis results by integrating the data. A multi graph-kernel based transfer learning method is proposed to tackle with this problem. Several different graph kernels are employed to mine structure information from brain network data and measure the similarity between brain networks. Then, the performance of transfer learning model is improved by a proposed multi-kernel learning framework. Experiments on the multi-center dataset of autistic spectrum disorder(ASD) indicate the classification performance on brain network data is improved and the advantages of different graph kernels are efficiently utilized by multi-kernel learning framework.
Key wordsBrain Network      Graph Kernel      Multi-center Data      Multi-source Transfer Learning      Multi-Kernel Learning     
Received: 27 February 2020     
ZTFLH: TP 391.4  
Fund:National Natural Science Foundation of China(No.61861130366,61876082,61732006)
Corresponding Authors: ZHANG Daoqiang, Ph.D., professor. His research inte-rests include pattern recognition, machine learning and medical image processing.   
About author:: JIANG You, master student. His research interests include medical image processing. ZHANG Junyi, master student. Her research interests include medical image processing.
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JIANG You
ZHANG Daoqiang
ZHANG Junyi
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JIANG You,ZHANG Daoqiang,ZHANG Junyi. Multi-graph Kernel Based Transfer Learning Method[J]. , 2020, 33(6): 488-495.
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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202006002      OR     http://manu46.magtech.com.cn/Jweb_prai/EN/Y2020/V33/I6/488
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