<ul><li>Outlier detection in high-dimensional tabular data is challenging due to the Multiple Views effect, where data is distributed across multiple lower-dimensional subspaces.</li><li>A new theoretical framework called Myopic Subspace Theory (MST) is introduced, which mathematically formulates the Multiple Views effect and writes subspace selection as a stochastic optimization problem.</li><li>V-GAN, a generative method trained based on MST, is presented to avoid exhaustive search over the feature space while preserving the intrinsic data structure.</li><li>Experiments on real-world datasets demonstrate that using V-GAN subspaces leads to improved one-class classification performance compared to existing methods, confirming the theoretical guarantees and practical viability of the approach.</li></ul>

Adversarial Subspace Generation for Outlier Detection in High-Dimensional Data

Discover more