面向自闭症辅助诊断的无监督模糊特征学习新方法

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Title:

A novel unsupervised fuzzy feature learning method for computer-aided diagnosis of autism

作者:

张英, 王骏, 鲍国强, 张春香, 王士同

江南大学 数字媒体学院, 江苏 无锡 214122

Author(s):

ZHANG Ying, WANG Jun, BAO Guoqiang, ZHANG Chunxiang, WANG Shitong

School of Digital Media, Jiangnan University, Wuxi 214122, China

关键词:

自闭症; 功能磁共振成像; 功能连接; 皮尔森相关性; 特征选择; 无监督模糊特征映射; 流形正则化框架; 支持向量机

Keywords:

autism; functional magnetic resonance imaging; functional connectivity; Pearson’s correlation; feature selection; unsupervised fuzzy feature mapping; manifold regularization framework; support vector machine

分类号:

TP391

DOI:

10.11992/tis.201808005

摘要:

自闭症患者的行为和认知缺陷与潜在的脑功能异常有关。对于静息态功能磁振图像（functional magnetic resonance imaging， fMRI）高维特征，传统的线性特征提取方法不能充分提取其中的有效信息用于分类。为此，本文面向fMRI数据提出一种新型的无监督模糊特征映射方法，并将其与多视角支持向量机相结合，构建分类模型应用于自闭症的计算机辅助诊断。该方法首先采用多输出TSK模糊系统的规则前件学习方法，将原始特征数据映射到线性可分的高维空间；然后引入流形正则化学习框架，提出新型的无监督模糊特征学习方法，从而得到原输出特征向量的非线性低维嵌入表示；最后使用多视角SVM算法进行分类。实验结果表明：本文方法能够有效提取静息态fMRI数据中的重要特征，在保证模型具有优越且稳定的分类性能的前提下，还可以提高模型的可解释性。

Abstract:

Studies have shown that the behavioral and cognitive defect of patients with autism have a close relationship with potential brain dysfunction. For the high-dimensional rs-fMRI features, traditional linear feature extraction method cannot always discriminatively extract the important information for classification. To this end, a novel method for fMRI data based on both unsupervised fuzzy feature mapping and multi-view support vector machine is proposed in this study, which aims to build a classification model for computer aided diagnosis of autism. In this method, the original features are first mapped to a linear separable high-dimensional space using the rule precursor learning method of multi-output Takagi-Sugeno-Kang (TSK) fuzzy system; then the manifold regularization learning framework is introduced. On the basis of this, a novel unsupervised fuzzy feature learning method is used to obtain the nonlinear low-dimensional embedding representation of the original output eigenvector. Finally, a multi-view support vector machine (SVM) algorithm is used for classification. The experimental results show that the proposed method can effectively extract important features from the rs-fMRI data and improve the interpretability of the model on the premise of ensuring a superior and stable classification performance of the model.