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.
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2020, Vol. 33 
