RCA: A Deep Collaborative Autoencoder Approach for Anomaly Detection.
RCA: A Deep Collaborative Autoencoder Approach for Anomaly Detection.
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DOI：
10.24963/ijcai.2021/208
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发表时间：
2021-08
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无监督异常检测（AD）在许多关键应用中起着至关重要的作用。在深度学习成功的推动下，近年来人们对将深度神经网络（DNN）应用于AD问题的兴趣越来越大。一种常见的方法是使用自动编码器来学习数据中正常观测的特征表示。自动编码器的重建误差然后被用作离群值分数来检测异常。然而，由于DNN的过度参数化带来的高复杂性，异常的重建误差也可能很小，这阻碍了这些方法的有效性。为了缓解这个问题，我们提出了一个强大的框架，使用协作自动编码器来共同识别正常的观察数据，同时学习其特征表示。我们研究了该框架的理论特性，并在经验上展示了其与其他基于DNN的方法相比的出色性能。实证结果还表明，与其他基线方法相比，框架对缺失值的弹性。
Unsupervised anomaly detection (AD) plays a crucial role in many critical applications. Driven by the success of deep learning, recent years have witnessed growing interest in applying deep neural networks (DNNs) to AD problems. A common approach is using autoencoders to learn a feature representation for the normal observations in the data. The reconstruction error of the autoencoder is then used as outlier score to detect the anomalies. However, due to the high complexity brought upon by over-parameterization of DNNs, the reconstruction error of the anomalies could also be small, which hampers the effectiveness of these methods. To alleviate this problem, we propose a robust framework using collaborative autoencoders to jointly identify normal observations from the data while learning its feature representation. We investigate the theoretical properties of the framework and empirically show its outstanding performance as compared to other DNN-based methods. Empirical results also show resiliency of the framework to missing values compared to other baseline methods.
影响因子：
3.1
作者：
Prasad, Nadipuram R.;Almanza-Garcia, Salvador;Lu, Thomas T.
通讯作者：
Lu, Thomas T.