一种基于鲁棒自编码器集成的文本异常检测方法
A Robust Autoencoder Ensemble-Based Approach for Anomaly Detection in Text
AI总结：
针对文本异常检测研究不足、未区分独立与上下文异常的问题，提出TAC文本异常污染机制和RoSAE鲁棒自编码器集成方法，在8个数据集上表现优于现有方法且鲁棒性更强。
摘要：
异常检测（AD）在视觉、时序等成熟应用领域是发展迅速的热门方向，这些领域已有丰富研究成果，但文本领域的异常检测才刚兴起。近年带自注意力机制的自监督方法是最主流的选择，现有工作已为前沿方法的构建和基准测试奠定了基础，本文提出两项核心贡献：上下文异常污染机制和一种新型集成方法。我们的方法Textual Anomaly Contamination（TAC，文本异常污染）可向正常类样本中注入独立异常或上下文异常，现有文献尚未对这两类异常做区分。为检测上下文异常，我们提出RoSAE：鲁棒子空间局部恢复自编码器集成，集成中的每个自编码器通过局部流形学习生成不同的潜在表示。基准测试表明，我们的方法在独立异常和上下文异常检测任务上均优于近年相关工作，且鲁棒性更强。我们还提供了8个数据集的对比实验，而非仅依赖Reuters和20 Newsgroups语料。
英文摘要：
Anomaly detection (AD) is a fast growing and popular domain among established applications like vision and time series. We observe a rich literature for these applications, but anomaly detection in text is only starting to blossom. Recently, self-supervised methods with self-attention mechanism have been the most popular choice. While recent works have proposed a working ground for building and benchmarking state of the art approaches, we propose two principal contributions in this paper: contextual anomaly contamination and a novel ensemble-based approach. Our method, Textual Anomaly Contamination (TAC), allows to contaminate inlier classes with either independent or contextual anomalies. In the literature, it appears that this distinction is not performed. For finding contextual anomalies, we propose RoSAE, a Robust Subspace Local Recovery Autoencoder Ensemble. All autoencoders of the ensemble present a different latent representation through local manifold learning. Benchmark shows that our approach outperforms recent works on both independent and contextual anomalies, while being more robust. We also provide 8 dataset comparison instead of only relying to Reuters and 20 Newsgroups corpora.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
