Generative Subspace Adversarial Active Learning for Outlier Detection in Multiple Views of High-dimensional Data

TL;DR

GSAAL is a novel generative adversarial network-based method designed for outlier detection in high-dimensional, multi-view data, effectively addressing inlier assumption, curse of dimensionality, and multiple views.

Contribution

It introduces GSAAL, a new adversarial active learning approach that models data distributions across multiple views, with proven convergence and scalability.

Findings

GSAAL outperforms existing outlier detection methods in multi-view high-dimensional data.

The method demonstrates strong scalability and convergence guarantees.

Experimental results confirm GSAAL's superior detection accuracy.

Abstract

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.