  Support Vector Data Description is a popular method for outlier detection.
However, its usefulness largely depends on selecting good hyperparameter values
-- a difficult problem that has received significant attention in literature.
Existing methods to estimate hyperparameter values are purely heuristic, and
the conditions under which they work well are unclear. In this article, we
propose LAMA (Local Active Min-Max Alignment), the first principled approach to
estimate SVDD hyperparameter values by active learning. The core idea bases on
kernel alignment, which we adapt to active learning with small sample sizes. In
contrast to many existing approaches, LAMA provides estimates for both SVDD
hyperparameters. These estimates are evidence-based, i.e., rely on actual class
labels, and come with a quality score. This eliminates the need for manual
validation, an issue with current heuristics. LAMA outperforms state-of-the-art
competitors in extensive experiments on real-world data. In several cases, LAMA
even yields results close to the empirical upper bound.
