论文标题
COVID-19使用张量方法分析
Covid-19 Analysis Using Tensor Methods
论文作者
论文摘要
在本文中，我们使用张量模型来分析COVID-19-19大流行数据。首先，我们使用张量模型，规范的多核和高阶塔克分解，以在多种模式上提取图案。其次，我们使用规范的多层张量分解实现了张量完成算法，以预测来自多个空间源的时空数据并识别Covid-19-19 Hotspots。我们将使用实用的正则化参数估计器应用正规迭代张量完成技术来预测COVID-19病例的传播并查找和识别热点。我们的方法可以高精度预测每周和每季度的COVID-19传播。第三，我们使用一种新颖的采样方法分析了美国的COVID-19数据，用于交替最小二乘。此外，我们将算法与标准张量分解的解释性，可视化和成本分析进行了比较。最后，我们通过将技术应用于新泽西州的Covid-19数据来证明这些方法的功效。
In this paper, we use tensor models to analyze Covid-19 pandemic data. First, we use tensor models, canonical polyadic and higher-order Tucker decompositions, to extract patterns over multiple modes. Second, we implement a tensor completion algorithm using canonical polyadic tensor decomposition to predict spatiotemporal data from multiple spatial sources and to identify Covid-19 hotspots. We apply a regularized iterative tensor completion technique with a practical regularization parameter estimator to predict the spread of Covid-19 cases and to find and identify hotspots. Our method can predict weekly and quarterly Covid-19 spreads with high accuracy. Third, we analyze Covid-19 data in the US using a novel sampling method for alternating least-squares. Moreover, we compare the algorithms with standard tensor decompositions in terms of their interpretability, visualization and cost analysis. Finally, we demonstrate the efficacy of the methods by applying the techniques to New Jersey's Covid-19 data.
