%0 Journal Article %A DONG Yingzhao1 %A WANG Bin1 %A MA Sasa1 %A LIU Hui1 %A XIONG Xin1 %A XUE Jie2 %T Dimension reduction method research of brain network status observation matrix based on t-SNE %D 2018 %R 10.3778/j.issn.1002-8331.1608-0250 %J Computer Engineering and Applications %P 42-47 %V 54 %N 1 %X 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 experimental 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 universality of this method. %U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.1608-0250