一种新的高维孤立点识别的几何方法
A Novel Geometric Approach for Outlier Recognition in High Dimension
摘要：
离群点识别是数据分析中的一个基本问题，在过去的几十年里引起了人们的极大关注。然而，大多数现有的方法仍然遭受几个问题，如高的时间和空间复杂度或不稳定的性能为不同的数据集。本文提出了一种新的基于优美几何技术"核心集”的高维离群点识别算法。该算法仅需线性时间复杂度和空间复杂度，理论质量得到了很好的保证。与现有方法相比的另一个优点是，我们的算法可以自然地扩展到处理多类内点。我们的实验结果表明，我们的算法优于现有的算法在随机和基准数据集。
英文摘要：
Outlier recognition is a fundamental problem in data analysis and has attracted a great deal of attention in the past decades. However, most existing methods still suffer from several issues such as high time and space complexities or unstable performances for different datasets. In this paper, we provide a novel algorithm for outlier recognition in high dimension based on the elegant geometric technique ``core-set". The algorithm needs only linear time and space complexities and achieves a solid theoretical quality guarantee. Another advantage over the existing methods is that our algorithm can be naturally extended to handle multi-class inliers. Our experimental results show that our algorithm outperforms existing algorithms on both random and benchmark datasets.
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。
