论文阅读 [TPAMI-2022] Active Surveillance via Group Sparse Bayesian Learning

论文阅读 [TPAMI-2022] Active Surveillance via Group Sparse Bayesian Learning

论文搜索(studyai.com)

搜索论文: Active Surveillance via Group Sparse Bayesian Learning

搜索论文: http://www.studyai.com/search/whole-site/?q=Active+Surveillance+via+Group+Sparse+Bayesian+Learning

关键字(Keywords)

Surveillance; Task analysis; Sensors; Bayes methods; Infectious diseases; Interpolation; Epidemic dynamics; diffusion; sensor deployment; dynamical systems; automatic relevance determination

机器视觉; 系统与控制

动态系统; 时间与空间

摘要(Abstract)

The key to the effective control of a diffusion system lies in how accurately we could predict its unfolding dynamics based on the observation of its current state.

有效控制扩散系统的关键在于，我们可以根据对扩散系统当前状态的观察，准确地预测其展开动力学。.

However, in the real-world applications, it is often infeasible to conduct a timely and yet comprehensive observation due to resource constraints.

然而，在实际应用中，由于资源限制，往往无法进行及时而全面的观测。.

In view of such a practical challenge, the goal of this work is to develop a novel computational method for performing active observations, termed active surveillance, with limited resources.

鉴于这一实际挑战，这项工作的目标是开发一种新的计算方法，用有限的资源进行主动观测，称为主动监视。.

Specifically, we aim to predict the dynamics of a large spatio-temporal diffusion system based on the observations of some of its components.

具体来说，我们的目标是基于对其某些组成部分的观测来预测大型时空扩散系统的动力学。.

Towards this end, we introduce a novel measure, the $\mathbf{\gamma }$γ value, that enables us to identify the key components by means of modeling a sentinel network with a row sparsity structure.

为此，我们引入了一种新的度量方法$\mathbf{\gamma }$γ值，它使我们能够通过对具有行稀疏结构的哨兵网络建模来识别关键组件。.

Having obtained a theoretical understanding of the $\mathbf{\gamma }$γ value, we design a backward-selection sentinel network mining algorithm (SNMA) for deriving the sentinel network via group sparse Bayesian learning.

在对$\mathbf{\gamma }$γ值有了理论上的理解之后，我们设计了一种反向选择哨兵网络挖掘算法（SNMA），用于通过群体稀疏贝叶斯学习导出哨兵网络。.

In order to be practically useful, we further address the issue of scalability in the computation of SNMA, and moreover, extend SNMA to the case of a non-linear dynamical system that could involve complex diffusion mechanisms.

为了实用，我们进一步讨论了SNMA计算中的可伸缩性问题，并且将SNMA扩展到可能涉及复杂扩散机制的非线性动力系统的情况。.

We show the effectiveness of SNMA by validating it using both synthetic datasets and five real-world datasets.

我们通过使用合成数据集和五个真实数据集来验证SNMA的有效性。.

The experimental results are appealing, which demonstrate that SNMA readily outperforms the state-of-the-art methods…

实验结果很有吸引力，这表明SNMA很容易优于最先进的方法。。.

作者(Authors)

[‘Hongbin Pei’, ‘Bo Yang’, ‘Jiming Liu’, ‘Kevin Chen-Chuan Chang’]