通过增加零值维度，用余弦相似度检测多维数据异常点
Outlier Detection Using Vector Cosine Similarity by Adding a Dimension
- 在原数据上新增全零维度构造新数据集
- 以非零点为观测原点，计算向量余弦相似度识别异常
- 方法简单高效，适合高维数据异常检测场景
我们提出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，通过在原始数据基础上添加一个全零值的新维度构建新数据集。当新数据集中某一点被选为测量点时，以该点为基准创建一个观测点，该观测点仅在新维度上具有非零值，其余维度与测量点相同。随后，从观测点分别连接到测量点及其他数据点形成向量，并通过比较这些向量之间的余弦相似度来识别异常数据。该方法实现了一个优化版本（MDOD），已发布于PyPI：https://pypi.org/project/mdod/。
原文摘要 · Abstract (English)
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/.
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