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OALib Journal期刊
ISSN: 2333-9721
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判别性子图挖掘方法及其在mci分类中的应用
DOI: 10.13232/j.cnki.jnju.2015.02.017, PP. 328-334
Keywords: 轻度认知障碍,结构连接网络,频繁子图挖掘,图核
Abstract:
最近，脑连接网络已经被用于神经退行性疾病（如阿尔茨海默病ad以及轻度认知障碍mci）的诊断和分类。以往典型方法是从脑连接网络中提取一些特征（如局部聚类系数等）构成一个长特征向量，并用其训练一个分类器用于最终的分类。然而，上述方法的一个缺点是未能充分考虑网络的拓扑结构信息，从而限制了分类性能的进一步提升。有鉴于此，本文提出了一种基于判别子图挖掘的脑连接网络分类方法。首先分别从正类训练样本集和负类训练样本集中挖掘频繁子网络（即频繁子图）。然后，利用基于图核的方法来衡量频繁子网络的判别性能，并选择那些最具判别性的频繁子网络作为判别子网络用于后续的分类。最后，在真实mci数据集上的实验验证了本文方法的有效性。
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