异常子空间稀疏PCA用于异常检测与解释
Abnormal Subspace Sparse PCA for Anomaly Detection and Interpretation
AI总结：
本文提出基于PCA的可解释异常检测模型，通过稀疏正交加载向量构建异常子空间，实现对检测到的异常进行解释，实验表明其检测性能与PCA相当。
摘要：
基于主成分分析（PCA）的异常检测模型的主要不足在于其可解释性。本文旨在提出一个可解释的PCA-based模型用于异常检测和解释。所提出的ASPCA模型通过稀疏且正交的加载向量构建异常子空间，并利用这些向量来解释检测到的异常。我们在合成数据集和两个现实世界数据集上的实验表明，所提出的ASPCA模型在检测准确性上与PCA模型相当，并能为个别异常提供解释。
英文摘要：
The main shortage of principle component analysis (PCA) based anomaly detection models is their interpretability. In this paper, our goal is to propose an interpretable PCA-based model for anomaly detection and interpretation. The propose ASPCA model constructs principal components with sparse and orthogonal loading vectors to represent the abnormal subspace, and uses them to interpret detected anomalies. Our experiments on a synthetic dataset and two real world datasets showed that the proposed ASPCA models achieved comparable detection accuracies as the PCA model, and can provide interpretations for individual anomalies.
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。
