CoMadOut——一种基于CoMAD的鲁棒离群点检测算法
CoMadOut -- A Robust Outlier Detection Algorithm based on CoMAD
AI总结：
本文提出基于CoMAD的鲁棒离群点检测算法CoMadOut，通过comedian PCA和分布内/外度量实现鲁棒评分，在AP、AUPRC和AUROC指标上与传统及深度方法具有竞争力。
摘要：
无监督学习方法在异常检测领域已经相当成熟，并在离群点数据集上取得了当前最先进的性能。离群点具有重要作用，因为它们有可能扭曲机器学习算法在给定数据集上的预测结果。尤其是在基于PCA的方法中，离群点对结果具有额外的破坏性潜力：它们不仅可能扭曲主成分的方向和平移，还会使离群点的检测变得更加困难。为了解决这一问题，我们提出了鲁棒的离群点检测算法CoMadOut，该算法满足两个必要性质：（1）对离群点具有鲁棒性，（2）能够检测离群点。我们的CoMadOut离群点检测变体使用comedian PCA，根据其变体不同，通过分布内度量（变体CMO）定义具有鲁棒噪声边界的正常区域，并通过分布外度量（变体CMO*）优化得分，例如CMO+k的峰度加权。这些度量允许对每个主成分进行基于分布的离群点评分，从而在正常实例和异常实例之间实现离群程度的适当对齐。将CoMadOut与传统方法、深度方法以及其他可比的鲁棒离群点检测方法进行比较的实验表明，所提出的CoMadOut方法在平均精度（AP）、精确率-召回率曲线下面积（AUPRC）和受试者工作特征曲线下面积（AUROC）方面与成熟方法具有竞争力。总之，我们的方法可以被视为离群点检测任务的一种鲁棒替代方案。
英文摘要：
Unsupervised learning methods are well established in the area of anomaly detection and achieve state of the art performances on outlier datasets. Outliers play a significant role, since they bear the potential to distort the predictions of a machine learning algorithm on a given dataset. Especially among PCA-based methods, outliers have an additional destructive potential regarding the result: they may not only distort the orientation and translation of the principal components, they also make it more complicated to detect outliers. To address this problem, we propose the robust outlier detection algorithm CoMadOut, which satisfies two required properties: (1) being robust towards outliers and (2) detecting them. Our CoMadOut outlier detection variants using comedian PCA define, dependent on its variant, an inlier region with a robust noise margin by measures of in-distribution (variant CMO) and optimized scores by measures of out-of-distribution (variants CMO*), e.g. kurtosis-weighting by CMO+k. These measures allow distribution based outlier scoring for each principal component, and thus, an appropriate alignment of the degree of outlierness between normal and abnormal instances. Experiments comparing CoMadOut with traditional, deep and other comparable robust outlier detection methods showed that the performance of the introduced CoMadOut approach is competitive to well established methods related to average precision (AP), area under the precision recall curve (AUPRC) and area under the receiver operating characteristic (AUROC) curve. In summary our approach can be seen as a robust alternative for outlier detection tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
