  Outlier detection in high-dimensional tabular data is an important task in
data mining, essential for many downstream tasks and applications. Existing
unsupervised outlier detection algorithms face one or more problems, including
inlier assumption (IA), curse of dimensionality (CD), and multiple views (MV).
To address these issues, we introduce Generative Subspace Adversarial Active
Learning (GSAAL), a novel approach that uses a Generative Adversarial Network
with multiple adversaries. These adversaries learn the marginal class
probability functions over different data subspaces, while a single generator
in the full space models the entire distribution of the inlier class. GSAAL is
specifically designed to address the MV limitation while also handling the IA
and CD, being the only method to do so. We provide a comprehensive mathematical
formulation of MV, convergence guarantees for the discriminators, and
scalability results for GSAAL. Our extensive experiments demonstrate the
effectiveness and scalability of GSAAL, highlighting its superior performance
compared to other popular OD methods, especially in MV scenarios.
