In addition to classification and regression, outlier detection has emerged as a relevant activity in deep learning. In comparison with previous approaches where the original features of the examples were used for separating the examples with high dissimilarity from the rest of the examples, deep learning can automatically extract useful features from raw data, thus removing the need for most of the feature engineering efforts usually required with classical machine learning approaches. This requires training the deep learning algorithm with labels identifying the examples or with numerical values. Although outlier detection in deep learning has been usually undertaken by training the algorithm with categorical labels—classifier—, it can also be performed by using the algorithm as regressor. Nowadays numerous urban areas have deployed a network of sensors for monitoring multiple variables about air quality. The measurements of these sensors can be treated individually—as time series—or collectively. Collectively, a variable monitored by a network of sensors can be transformed into a map. Maps can be used as images in machine learning algorithms—including computer vision algorithms—for outlier detection. The identification of anomalous episodes in air quality monitoring networks allows later processing this time period with finer‐grained scientific packages involving fluid dynamic and chemical evolution software, or the identification of malfunction stations. In this work, a Convolutional Neural Network is trained—as a regressor—using as input Ozone‐urban images generated from the Air Quality Monitoring Network of Madrid (Spain). The learned features are processed by Density‐based Spatial Clustering of Applications with Noise (DBSCAN) algorithm for identifying anomalous maps. Comparisons with other deep learning architectures are undertaken, for instance, autoencoders—undercomplete and denoizing—for learning salient features of the maps and later to use as input of DBSCAN. The proposed approach is able efficiently find maps with local anomalies compared to other approaches based on raw images or latent features extracted with autoencoders architectures with DBSCAN.

中文翻译：

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在城市臭氧图中检测异常事件

除了分类和回归之外，离群值检测已成为深度学习中的一项相关活动。与以前的方法（其中示例的原始特征用于将示例与其他示例之间的差异较大）分开相比，深度学习可以自动从原始数据中提取有用的特征，从而省去了大部分特征工程工作通常是经典机器学习方法所必需的。这需要使用识别示例的标签或数值来训练深度学习算法。尽管深度学习中的异常检测通常是通过使用分类标签-分类器训练该算法来进行的，但也可以通过将该算法用作回归器来执行。如今，许多城市地区已经部署了传感器网络，用于监视有关空气质量的多个变量。这些传感器的测量值可以作为时间序列单独处理，也可以统一处理。总的来说，由传感器网络监视的变量可以转换为地图。地图可以在机器学习算法（包括计算机视觉算法）中用作图像，用于离群值检测。通过空气质量监测网络中异常事件的识别，可以稍后使用包含流体动力学和化学演化软件的细粒度科学软件包或故障站的识别来对该时间段进行后续处理。在这项工作中，使用从马德里（西班牙）空气质量监测网络生成的臭氧-城市图像作为输入，对卷积神经网络进行了训练（作为回归器）。通过基于密度的带噪声应用程序空间聚类（DBSCAN）算法处理学习到的功能，以识别异常图。与其他深度学习架构进行了比较，例如，自动编码器（不完整和去噪）用于学习地图的显着特征，以后用作DBSCAN的输入。与其他方法相比，与基于DBSCAN的自动编码器体系结构提取的原始图像或潜在特征的其他方法相比，该方法能够有效地查找具有局部异常的地图。自动编码器（不完整和去噪），用于学习地图的显着特征，以后用作DBSCAN的输入。与其他方法相比，与基于DBSCAN的自动编码器体系结构提取的原始图像或潜在特征的其他方法相比，该方法能够有效地查找具有局部异常的地图。自动编码器（不完整和去噪），用于学习地图的显着特征，以后用作DBSCAN的输入。与其他方法相比，与基于DBSCAN的自动编码器体系结构提取的原始图像或潜在特征的其他方法相比，该方法能够有效地查找具有局部异常的地图。