Detecting spatio-temporal modes in multivariate data by entropy field decomposition
Detecting spatio-temporal modes in multivariate data by entropy field decomposition
复制标题
DOI：
10.1088/1751-8113/49/39/395001
复制
发表时间：
2016-09-30
影响因子：
2.1
通讯作者：
Galinsky, Vitaly L.
中科院分区：
文献类型：
--
作者：
Frank, Lawrence R.;Galinsky, Vitaly L.
提出了一种新的数据分析方法，该方法解决了多元数据中检测时空变化的一般问题。该方法利用两种近期和免费的一般方法来进行数据分析，信息场理论（IFT）和熵光谱途径（ESPS）。两种方法都重新制定并结合了贝叶斯理论，因此使用先前的信息来揭示未知信号的潜在结构。 ESP和IFT的统一创建了一种非高斯和非线性的方法，并发现可以根据其重要性对信号行为的独特时空模式产生独特的时空模式，参数变化的时空轨迹可以从中进行。构造和量化。还介绍了该理论在分析具有完全不同的，无关的性质（缺乏任何潜在相似性的数据）中的现实世界应用的两个简短示例。第一个示例提供了静止状态功能磁共振成像数据的分析，该数据使我们能够创建一种评估和分类大脑活动的有效，准确的计算方法。第二个示例证明了该方法在龙卷风开发和形成的复杂阶段使用移动多普勒雷达记录的数据中对强大大气风暴循环系统进行分析的潜力。该方法的参考实现将作为Quest工具包的一部分提供，该工具包目前正在成像中科学计算中心开发。
A new data analysis method that addresses a general problem of detecting spatio-temporal variations in multivariate data is presented. The method utilizes two recent and complimentary general approaches to data analysis, information field theory (IFT) and entropy spectrum pathways (ESPs). Both methods reformulate and incorporate Bayesian theory, thus use prior information to uncover underlying structure of the unknown signal. Unification of ESP and IFT creates an approach that is non-Gaussian and nonlinear by construction and is found to produce unique spatio-temporal modes of signal behavior that can be ranked according to their significance, from which space-time trajectories of parameter variations can be constructed and quantified. Two brief examples of real world applications of the theory to the analysis of data bearing completely different, unrelated nature, lacking any underlying similarity, are also presented. The first example provides an analysis of resting state functional magnetic resonance imaging data that allowed us to create an efficient and accurate computational method for assessing and categorizing brain activity. The second example demonstrates the potential of the method in the application to the analysis of a strong atmospheric storm circulation system during the complicated stage of tornado development and formation using data recorded by a mobile Doppler radar. Reference implementation of the method will be made available as a part of the QUEST toolkit that is currently under development at the Center for Scientific Computation in Imaging.