SUOD: Toward Scalable Unsupervised Outlier Detection

Understand

Outlier detection is a key field of machine learning for identifying abnormal data objects.

• Due to the high expense of acquiring ground truth, unsupervised models are often chosen in practice.
• To compensate for the unstable nature of unsupervised algorithms, practitioners from high-stakes fields like finance, health, and security, prefer to build a large number of models for further combination and analysis.
• However, this poses scalability challenges in high-dimensional large datasets.