Adaptive Local Kernels Formulation of Mutual Information with Application to Active Post-Seismic Building Damage Inference
Adaptive Local Kernels Formulation of Mutual Information with Application to Active Post-Seismic Building Damage Inference
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DOI：
10.1016/j.ress.2021.107915
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发表时间：
2021-05
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影响因子：
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通讯作者：
M. Sheibani;Ge Ou
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文献类型：
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作者：
M. Sheibani;Ge Ou
在各种监督学习应用中，不能保证训练数据的丰富性。其中一种情况是地震后建筑物的区域损害评估。查询每栋建筑的损坏标签需要专家进行彻底的检查，因此是一项昂贵的任务。一种实用的方法是在顺序学习方案中对最有信息的建筑进行抽样。主动学习方法推荐信息量最大的案例，能够最大限度地减少泛化误差。互信息（MI）的信息论度量可以最大化输入域的预期信息增益，可用于基于池的场景中数据集的信息采样。然而，标准MI算法的计算复杂性阻碍了该方法在大型数据集上的应用。为了降低计算成本，提出了一种局部核策略，但在该策略的原始制定中没有考虑核对观察到的标记的适应性。在本文中，开发了一种自适应局部核方法，使核与观察到的输出数据一致，同时提高了标准MI算法的计算复杂性。该算法与高斯过程回归（GPR）方法一起工作，其中核超参数在每次标签查询后使用最大似然估计进行更新。在序列学习过程中，更新后的超参数可用于MI核矩阵中，以提高样本建议的性能。在2018年AK安克雷奇地震的模拟中证明了该方法的优点。研究结果表明，虽然该算法使用较少的训练数据使探地雷达达到可接受的性能，但计算需求仍然低于标准的局部核策略。
The abundance of training data is not guaranteed in various supervised learning applications. One of these situations is the post-earthquake regional damage assessment of buildings. Querying the damage label of each building requires a thorough inspection by experts, and thus, is an expensive task. A practical approach is to sample the most informative buildings in a sequential learning scheme. Active learning methods recommend the most informative cases that are able to maximally reduce the generalization error. The information-theoretic measure of mutual information (MI), which maximizes the expected information gain over the input domain, can be used for informative sampling of a dataset in a pool-based scenario. However, the computational complexity of the standard MI algorithm prevents the utilization of this method on large datasets. A local kernels strategy was proposed to reduce the computational costs, but the adaptability of the kernels to the observed labels was not considered in the original formulation of this strategy. In this article, an adaptive local kernels methodology is developed that enables the conformability of the kernels to the observed output data while enhancing the computational complexity of the standard MI algorithm. The proposed algorithm is developed to work with a Gaussian process regression (GPR) method, where the kernel hyperparameters are updated after each label query using maximum likelihood estimation. In the sequential learning procedure, the updated hyperparameters can be used in the MI kernel matrices to improve the sample suggestion performance. The advantages of the proposed method are demonstrated in a simulation of the 2018 Anchorage, AK, earthquake. It is shown that while the proposed algorithm enables GPR to reach acceptable performance using fewer training data, the computational demand remains lower than the standard local kernels strategy.