Outlier Detect using Vector Cosine Similarity by Adding a Dimension

We propose a new outlier detection method with multi-dimensional data. The method detect outliers based on vector cosine similarity, with a new dataset built by adding a zero value dimension to original data. When a point in the new dataset is chosen as a measured point, an observation point is constructed as an origin with the only difference in the new dimension having a non-zero value when compared to the measured point. The vector from the observation point to the measured point is then formed, followed by another vector from the observation point to another point in the dataset. We compare the cosine similarity of the vectors to find out abnormal data.