  Anomalous pattern detection aims to identify instances where deviation from
normalcy is evident, and is widely applicable across domains. Multiple
anomalous detection techniques have been proposed in the state of the art.
However, there is a common lack of a principled and scalable feature selection
method for efficient discovery. Existing feature selection techniques are often
conducted by optimizing the performance of prediction outcomes rather than its
systemic deviations from the expected. In this paper, we proposed a
sparsity-based automated feature selection (SAFS) framework, which encodes
systemic outcome deviations via the sparsity of feature-driven odds ratios.
SAFS is a model-agnostic approach with usability across different discovery
techniques. SAFS achieves more than $3\times$ reduction in computation time
while maintaining detection performance when validated on publicly available
critical care dataset. SAFS also results in a superior performance when
compared against multiple baselines for feature selection.
