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.

高维表格数据中的离群点检测是数据挖掘中一项重要任务，本研究提出了一种名为 GSAAL 的生成子空间对抗主动学习方法，通过多个对手学习不同数据子空间上的边际类概率函数，同时在全空间模拟内群类的整个分布，以解决现有离群点检测算法中的多视图、内点假设和维度诅咒问题。该研究通过大量实验证明了 GSAAL 的有效性和可伸缩性，在多视图场景中表现优于其他流行离群点检测方法。

高维数据多视图异常检测的生成子空间对抗主动学习

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

Machine learning models that first learn a representation of a domain in
terms of human-understandable concepts, then use it to make predictions, have
been proposed to facilitate interpretation and interaction with models trained
on high-dimensional data. However these methods have important limitations: the
way they define concepts are not inherently interpretable, and they assume that
concept labels either exist for individual instances or can easily be acquired
from users. These limitations are particularly acute for high-dimensional
tabular features. We propose an approach for learning a set of transparent
concept definitions in high-dimensional tabular data that relies on users
labeling concept features instead of individual instances. Our method produces
concepts that both align with users' intuitive sense of what a concept means,
and facilitate prediction of the downstream label by a transparent machine
learning model. This ensures that the full model is transparent and intuitive,
and as predictive as possible given this constraint. We demonstrate with
simulated user feedback on real prediction problems, including one in a
clinical domain, that this kind of direct feedback is much more efficient at
learning solutions that align with ground truth concept definitions than
alternative transparent approaches that rely on labeling instances or other
existing interaction mechanisms, while maintaining similar predictive
performance.

提出了一种透明的机器学习模型学习方法，使用用户的反馈来标记概念特征，从而生成透明的概念定义，以提高模型的可解释性和预测性能。