Quantifying the abnormal degree of each instance within data sets to detect outlying instances, is an issue in unsupervised anomaly detection research. In this paper, we propose a robust anomaly detection method based on principal component analysis (PCA). Traditional PCA-based detection algorithmscommonly obtain a high false alarm for the outliers. The main reason is that ignores the difference of location and scale to each component of the outlier score, this leads to the cumulated outlier score deviates from the true values. To address the issue, we introduce the median and the Median Absolute Deviation (MAD) to rescale each outlier score that mapped onto the corresponding principal direction. And then, the true outlier scores of instances can be obtained as the sum of weighted squares of the rescaled scores. Also, the issue that the assignment of the weight for each outlier score will be solved. The main advantage of our new approach is easy to build with unsupervised data and the recognition performance is better than the classical PCA-based methods. We compare our method to the five different anomaly detection techniques, including two traditional PCA-based methods, in our experiment analysis. The experimental results show that the proposed method has a good performance for effectiveness, efficiency, and robustness.

中文翻译：

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基于主成分分析的鲁棒异常检测算法

量化数据集中每个实例的异常程度以检测异常实例是无监督异常检测研究中的一个问题。在本文中，我们提出了一种基于主成分分析（PCA）的鲁棒异常检测方法。传统的基于PCA的检测算法通常会为异常值获得较高的误报率。主要原因是忽略了离群值每个分量的位置和尺度的差异，这导致累积的离群值偏离了真实值。为了解决该问题，我们引入了中位数和中位数绝对偏差（MAD），以重新调整映射到相应主要方向的每个异常值。然后，实例的真实异常值可以作为重新缩放分数的加权平方之和获得。还，将解决每个离群值得分的权重分配问题。我们的新方法的主要优点是易于在无监督的数据下构建，并且识别性能优于基于PCA的经典方法。在实验分析中，我们将我们的方法与五种不同的异常检测技术进行了比较，包括两种基于PCA的传统方法。实验结果表明，该方法具有良好的有效性，效率和鲁棒性。在我们的实验分析中。实验结果表明，该方法具有良好的有效性，效率和鲁棒性。在我们的实验分析中。实验结果表明，该方法具有良好的有效性，效率和鲁棒性。