计算机工程与应用2018，Vol.54Issue(1)：42-47,6.DOI:10.3778/j.issn.1002-8331.1608-0250
基于t-SNE的脑网络状态观测矩阵降维方法研究
Dimension reduction method research of brain network status obser- vation matrix based on t-SNE
摘要
Abstract
The brain network state observation matrix based on fMRI reconstruction technology is in high dimension and characterless. A dimension reduction method based on t-distributed Stochastic Neighbor Embedding algorithm for this kind of matrix is presented and a platform for the dimension reduction and visualization is built with Python. The experi-mental results show that compared with popular dimension reduction methods, the low dimension embedding of brain network state observation matrix with this method demonstrates distinct clustering, and the dimension reduction results of different brain network state observation matrix show up some common regularity, which supports the validity and univer-sality of this method.关键词
高维数据降维/脑功能网络/脑网络状态观测矩阵/t-SNE算法Key words
high dimension reduction/functional brain network/brain network state observation matrix/t-SNE algorithm分类
信息技术与安全科学引用本文复制引用
董迎朝,王彬,马洒洒,刘辉,熊新,薛洁..基于t-SNE的脑网络状态观测矩阵降维方法研究[J].计算机工程与应用,2018,54(1):42-47,6.基金项目
国家自然科学基金(No.61263017). （No.61263017）
