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Innovations Autoencoder and its Application in One-class Anomalous Sequence detection

JOURNAL OF MACHINE LEARNING RESEARCH 2022年第1期23卷 1-27页

作者： Wang, Xinyi Tong, Lang Cornell Univ Dept Elect & Comp Engn Ithaca NY 14850 USA

An innovations sequence of a time series is a sequence of independent and identically distributed random variables with which the original time series has a causal representation. The innovation at a time is statistically independent of the history of the time series. As such, it represents the new information contained at present but not in the past. Because of its simple probability structure, the innovations sequence is the most efficient signature of the original. Unlike the principle or independent component representations, an innovations sequence preserves not only the complete statistical properties but also the temporal order of the original time series. An long-standing open problem is to find a computationally tractable way to extract an innovations sequence of non-Gaussian processes. This paper presents a deep learning approach, referred to as Innovations Autoencoder (IAE), that extracts innovations sequences using a causal convolutional neural network. An application of IAE to the one-class anomalous sequence detection problem with unknown anomaly and anomaly-free models is also presented.

关键词： Innovations sequence Generative adversary networks Autoencoder Out-of-distribution detection non-parametric anomaly detection

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non-parametric empirical machine learning for short-term and long-term structural health monitoring

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STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL 2022年第6期21卷 2700-2718页

作者： Entezami, Alireza Shariatmadar, Hashem De Michele, Carlo Politecn Milan Dept Civil & Environm Engn Piazza Leonardo Da Vinci 32 I-20133 Milan Italy Ferdowsi Univ Mashhad Fac Engn Dept Civil Engn Mashhad Razavi Khorasan Iran

Early damage detection is an initial step of structural health monitoring. Thanks to recent advances in sensing technology, the application of data-driven methods based on the concept of machine learning has significantly increased among civil engineers and researchers. On this basis, this article proposes a novel non-parametric anomaly detection method in an unsupervised learning manner via the theory of empirical machine learning. The main objective of this method is to define a new damage index by using some empirical measure and the concept of minimum distance value. For this reason, an empirical local density is initially computed for each feature and then multiplied by the minimum distance of that feature to derive a new damage index for decision-making. The minimum distance is obtained by calculating the distances between each feature and training samples and finding the minimum quantity. The major contributions of this research contain developing a novel non-parametric algorithm for decision-making under high-dimensional and low-dimensional features and proposing a new damage index. To detect early damage, a threshold boundary is computed by using the extreme value theory, generalized Pareto distribution, and peak-over-threshold approach. Dynamic and statistical features of two full-scale bridges are used to verify the effectiveness and reliability of the proposed non-parametric anomaly detection. In order to further demonstrate its accuracy and proper performance, it is compared with some classical and recently published anomaly detection techniques. Results show that the proposed non-parametric method can effectively discriminate a damaged state from its undamaged condition with high damage detectability and inconsiderable false positive and false negative errors. This method also outperforms the anomaly detection techniques considered in the comparative studies.

关键词： Structural health monitoring non-parametric anomaly detection empirical machine learning environmental variability bridges

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Innovations autoencoder and its application in one-class anomalous sequence detection

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2022年第1期23卷 2347-2373页

作者： Xinyi Wang Lang Tong Department of Electrical and Computer Engineering Cornell University Ithaca NY

关键词： innovations sequence generative adversary networks autoencoder out-of-distribution detection non-parametric anomaly detection

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