计算机工程2016，Vol.42Issue(12)：151-155,5.DOI:10.3969/j.issn.1000-3428.2016.12.027
基于相似度矩阵约减的仿射聚类fMRI数据分析
fMRI Data Analysis of Affinity Propagation Clustering Based on Similarity Matrix Reduction
摘要
Abstract
Affinity Propagation Clustering(APC)method shows its limitations in time complexity,data storage and clustering results while handling massive functional Magnetic Resonance Imaging(fMRI)data.Aiming at these problems,this paper proposes a new method named SDAPC,which combines Sparse APC(SAPC)with similarity matrix reduction.It starts from sparse approximation on fMRI data,continues with the density analysis on sparse data by Gaussian density function and Euclidean distance,and finally realizes the detection on the functional connectivity of reduced fMRI data.The task-related data experiment gets the following results:SDAPC produces a fine ROC curve for single subject while running about three times faster than SAPC.SDAPC and SAPC both get better ROC curves for multiple subjects than single subject.The resting-state data experiment leads to the further finding that SDAPC can successfully identify nine resting-state networks.关键词
仿射传播聚类/功能磁共振成像/时间复杂度/相似度矩阵约减/高斯密度函数Key words
Affinity Propagation Clustering(APC)/functional Magnetic Resonance Imaging(fMRI)/time complexity/similarity matrix reduction/Gaussian density function分类
信息技术与安全科学引用本文复制引用
管秀英,曾卫明,王倪传..基于相似度矩阵约减的仿射聚类fMRI数据分析[J].计算机工程,2016,42(12):151-155,5.基金项目
国家自然科学基金(31170952,31470954) （31170952,31470954）
上海市教育委员会科研创新重点项目(11ZZ143). （11ZZ143）
