A Large-scale Study on Unsupervised Outlier Model Selection: Do Internal Strategies Suffice?

Understand

Given an unsupervised outlier detection task, how should one select a detection algorithm as well as its hyperparameters (jointly called a model)? Unsupervised model selection is notoriously difficult, in the absence of hold-out validation data with ground-truth labels.

• Therefore, the problem is vastly understudied.
• In this work, we study the feasibility of employing internal model evaluation strategies for selecting a model for outlier detection.
• These so-called internal strategies solely rely on the input data (without labels) and the output (outlier scores) of the candidate models.