This article addresses the problem of outlier detection for wireless sensor networks. As increasing amounts of observational data are tending to be high-dimensional and large scale, it is becoming increasingly difficult for existing techniques to perform outlier detection accurately and efficiently. Although dimensionality reduction tools (such as deep belief network) have been utilized to compress the high-dimensional data to support outlier detection, these methods may not achieve the desired performance due to the special distribution of the compressed data. Furthermore, because most existed classification methods must solve a quadratic optimization problem in their training stage, they cannot perform well in large-scale datasets. In this article, we developed a new form of classification model called “deep belief network online quarter-sphere support vector machine,” which combines deep belief network with online quarter-sphere one-class support vector machine. Based on this model, we first propose a model training method that learns the radius of the quarter sphere by a sorting method. Then, an online testing method is proposed to perform online outlier detection without supervision. Finally, we compare the proposed method with the state of the arts using extensive experiments. The experimental results show that our method not only reduces the computational cost by three orders of magnitude but also improves the detection accuracy by 3%–5%.

中文翻译：

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无线传感器网络高维数据的快速离群检测

本文解决了无线传感器网络的异常值检测问题。随着越来越多的观测数据趋于高维和大规模，现有技术越来越难以准确有效地进行异常值检测。尽管已经使用降维工具（例如深度置信网络）来压缩高维数据以支持异常值检测，但由于压缩数据的特殊分布，这些方法可能无法达到预期的性能。此外，由于大多数现有分类方法在训练阶段必须解决二次优化问题，因此它们在大规模数据集上表现不佳。在本文中，我们开发了一种新形式的分类模型，称为“深度信念网络在线四分之一球支持向量机”，它结合了深度信念网络和在线四分之一球一类支持向量机。基于这个模型，我们首先提出了一种模型训练方法，通过排序的方法来学习四分之一球体的半径。然后，提出了一种在线测试方法来在没有监督的情况下进行在线异常值检测。最后，我们使用广泛的实验将所提出的方法与现有技术进行比较。实验结果表明，我们的方法不仅将计算成本降低了三个数量级，而且将检测精度提高了 3%–5%。我们首先提出了一种模型训练方法，通过排序方法学习四分之一球体的半径。然后，提出了一种在线测试方法来在没有监督的情况下进行在线异常值检测。最后，我们使用大量实验将所提出的方法与现有技术进行比较。实验结果表明，我们的方法不仅将计算成本降低了三个数量级，而且将检测精度提高了 3%–5%。我们首先提出了一种模型训练方法，通过排序方法学习四分之一球体的半径。然后，提出了一种在线测试方法来在没有监督的情况下进行在线异常值检测。最后，我们使用大量实验将所提出的方法与现有技术进行比较。实验结果表明，我们的方法不仅将计算成本降低了三个数量级，而且将检测精度提高了 3%–5%。