基于多图核的迁移学习方法

doi:10.16451/j.cnki.issn1003-6059.202006002

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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 words： Brain 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

Cite this article:

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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