论文标题
稀疏网络建模
Sparse Network Modeling
论文作者
论文摘要
已经有许多尝试通过多元方法来识别高维网络功能的尝试。具体而言，当表示为p的体素或节点的数量大于表示为n的图像数量时，它会产生一个带有无限许多可能解决方案的不确定模型。通常通过将不确定的系统定向并施加较少的罚款来解决小N大P问题。流行的稀疏网络模型包括稀疏相关性，套索，稀疏的规范相关性和图形 - 拉索。这些流行的稀疏模型需要优化L1-Norm惩罚，这是解决大规模问题的主要计算瓶颈。因此，许多现有的脑成像中现有稀疏脑网络模型仅限于几百个或更少的节点。 2527个用于阿尔茨海默氏病的LASSO模型中使用的MRI特征可能是脑成像文献中任何稀疏模型中使用的最大特征。
There have been many attempts to identify high-dimensional network features via multivariate approaches. Specifically, when the number of voxels or nodes, denoted as p, are substantially larger than the number of images, denoted as n, it produces an under-determined model with infinitely many possible solutions. The small-n large-p problem is often remedied by regularizing the under-determined system with additional sparse penalties. Popular sparse network models include sparse correlations, LASSO, sparse canonical correlations and graphical-LASSO. These popular sparse models require optimizing L1-norm penalties, which has been the major computational bottleneck for solving large-scale problems. Thus, many existing sparse brain network models in brain imaging have been restricted to a few hundreds nodes or less. 2527 MRI features used in a LASSO model for Alzheimer's disease is probably the largest number of features used in any sparse model in the brain imaging literature.
