Prediction of urban noise is becoming more significant for tackling noise pollution and protecting human mental health. However, the existing noise prediction algorithms neglected not only the correlation between noise regions, but also the nonlinearity and sparsity of the data, which resulted in low accuracy of filling in the missing entries of data. In this paper, we propose a model based on multiple views and kernel-matrix tensor decomposition to predict the noise situation at different times of day in each region. We first construct a kernel tensor decomposition model by using kernel mapping in order to speed decomposition rate and realize stable estimate the prediction system. Then, we analyze and compute the cause of the noise from multiple views including computing the similarity of regions and the correlation between noise categories by kernel distance, which improves the credibility to infer the noise situation and the categories of regions. Finally, we devise a prediction algorithm based on the kernel-matrix tensor factorization model. We evaluate our method with a real dataset, and the experiments to verify the advantages of our method compared with other existing baselines.

中文翻译：

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基于多视图和核张量分解的城市噪声推理模型

城市噪声预测对于解决噪声污染和保护人类心理健康正变得越来越重要。然而，现有的噪声预测算法不仅忽略了噪声区域之间的相关性，而且忽略了数据的非线性和稀疏性，导致填补数据缺失条目的准确率不高。在本文中，我们提出了一种基于多视图和核矩阵张量分解的模型来预测每个区域一天中不同时间的噪声情况。我们首先利用核映射构建核张量分解模型，以加快分解速度，实现预测系统的稳定估计。然后，我们从多个角度分析和计算噪声的原因，包括计算区域的相似性和通过核距离计算噪声类别之间的相关性，从而提高推断噪声情况和区域类别的可信度。最后，我们设计了一种基于核矩阵张量分解模型的预测算法。我们使用真实数据集评估我们的方法，并通过实验验证我们的方法与其他现有基线相比的优势。