关键词: 子图挖掘, 特征选择, 图核降维, 脑网络分类

Abstract: Brain network classification methods have attracted a lot of attentions in the fields including brain science and brain disease diagnosis. However, most of existing studies on brain network classification use brain regions or the correlation between paired brain regions as classification features, which cannot reflect the topology information among multiple brain regions. To overcome the problem, this paper proposes a novel brain network classification method with subgraph selection and graph-kernel-based dimensionality reduction. Firstly, this method mines two groups of frequent subgraphs from positive and negative classes respectively, and then selects the most discriminative subgraphs using the subgraph selection algorithm based on their respective frequencies difference. Secondly, it uses the graph-kernel-based principal component analysis (GK-PCA) method to perform feature extraction on the graph data with selected subgraphs. Finally, it adopts the support vector machine (SVM) to perform classification on the data with extracted features, validates the proposed method on real brain network dataset of mild cognitive impairment (MCI), and the experimental results show the efficacy of the proposed method.

Key words: subgraph mining, feature selection, graph-kernel-based dimensionality reduction, brain network classification