Dimension Reduction for Outlier Detection Using DOBIN

This article introduces DOBIN, a new approach to select a set of basis vectors tailored for outlier detection. DOBIN has a simple mathematical foundation and can be used as a dimension reduction tool for outlier detection tasks. We demonstrate the effectiveness of DOBIN on an extensive data repository, by comparing the performance of outlier detection methods using DOBIN and other bases. We further illustrate the utility of DOBIN as an outlier visualization tool. The R package dobin implements this basis construction. Supplementary materials for this article are available online.

本文提出了DOBIN这一专为异常检测任务设计的基向量选择新方法。DOBIN具备简洁的数学理论基础，可作为异常检测任务中的降维工具使用。我们通过对比使用DOBIN与其他基向量的异常检测方法的性能，在大规模数据集仓库中验证了DOBIN的有效性。此外，我们还演示了DOBIN作为异常可视化工具的应用价值。R语言包dobin实现了该基向量构建方案。本文的补充材料可在线获取。