基于自适应稀疏结构学习的神经精神疾病特征选择方法

在计算机辅助诊断神经精神疾病研究中,需要专业人士为样本进行诊断级的语义标注,耗费大量时间和精力,因此,以无监督的方式开展神经精神疾病辅助诊断研究具有重要意义.文中提出基于自适应稀疏结构学习的无监督特征选择方法,用于精神分裂症和阿兹海默症辅助诊断.在统一框架下同时学习稀疏表示和数据流形结构,并在该框架中采用一般化范数对稀疏学习的重构误差进行建模,不断迭代更新数据集的流形结构,解决传统特征选择方法存在的鲁棒性不足问题.在精神分裂症和阿兹海默症两个公共数据集上的实验表明文中方法在神经精神疾病分类中的有效性

Feature Selection Method for Neuropsychiatric Disorder Based on Adaptive Sparse Structure Learning

In the research of computer-aided diagnosis techniques for neuropsychiatric diseases, professionals are required to perform diagnostic-level semantic annotations on samples, and it is time-consuming and labor-intensive. Therefore, it is of great importance to develop unsupervised techniques for the computer-aided diagnosis on neuropsychiatric diseases. In this paper, an unsupervised feature selection method based on adaptive sparse structure learning is proposed and applied to the task of diagnosis on Schizophrenia and Alzheimer′s disease. The sparse representation and the data manifold structure are simultaneously learned in a unified framework. In this framework, the generalized norm is adopted to model the reconstruction error of sparse learning. The manifold structure of the whole dataset is iteratively updated. The lacking of robustness in the traditional feature selection methods is relieved. Experiments on two public datasets of Schizophrenia and Alzheimer′s disease demonstrate the effectiveness of the proposed method in classification of neuropsychiatric diseases.