通过添加维度进行向量余弦相似度的异常检测
Outlier Detection Using Vector Cosine Similarity by Adding a Dimension
AI总结：
通过添加维度改进的向量余弦相似度方法用于多维数据异常检测，提供优化实现MDOD。
摘要：
我们提出了一种新的多维数据异常检测方法。该方法基于向量余弦相似度来检测异常，使用了一个新数据集，该数据集通过在原始数据上添加零值维度来构造。当新数据集中的一个点被选为测量点时，会创建一个观测点作为原点，仅在新维度上与测量点不同，具有非零值。然后从观测点到测量点和其他数据集中的点形成向量。通过比较这些向量的余弦相似度，可以识别异常数据。优化实现（MDOD）可在PyPI上获得：https://pypi.org/project/mdod/。
英文摘要：
We propose a new outlier detection method for multi-dimensional data. The method detects outliers based on vector cosine similarity, using a new dataset constructed by adding a dimension with zero values to the original data. When a point in the new dataset is selected as the measured point, an observation point is created as the origin, differing only in the new dimension by having a non-zero value compared to the measured point. Vectors are then formed from the observation point to the measured point and to other points in the dataset. By comparing the cosine similarities of these vectors, abnormal data can be identified. An optimized implementation (MDOD) is available on PyPI: https://pypi.org/project/mdod/.
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。
