L1-Depth Revisited: A Robust Angle-Based Outlier Factor in High-Dimensional Space

Ninh Pham

Abstract: Angle-based outlier detection (ABOD) has been recently emerged as an effective method to detect outliers in high dimensions. Instead of examining neighborhoods as proximity-based concepts, ABOD assesses the broadness of angle spectrum of a point as an outlier factor. Despite being a parameter-free and robust measure in high-dimensional space, the exact solution of ABOD suffers from the cubic cost \(O(n^3)\) regarding the data size n, hence cannot be used on large-scale data sets.