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

arXiv:2404.14451v1 Announce Type: new
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 …

abstract active learning adversarial algorithms applications arxiv cs.ai cs.lg data data mining detection dimensionality face generative mining multiple outlier tabular tabular data tasks type unsupervised