Contrastive Structured Anomaly Detection for Gaussian Graphical Models

Maurya, Abhinav; Cheung, Mark

doi:10.1109/asonam.2018.8508475

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…A linear Gaussian model (LGM) is a special type of BN that can model relationships between multiple continuous variables using conditional linear gaussian distributions. One significant advantage of LGMs is that they can model the causal relationships between variables once the graph structure is determined, enabling the modeling of realworld processes that generate MTS data [35], [36].…”

Section: B Linear Gaussian Modelmentioning

confidence: 99%

Machine Learning-Based Anomaly Detection for Multivariate Time Series With Correlation Dependency

Chauhan

Lee

2022

IEEE Access

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Recent advances in data collection facilitate the acquisition of large quantities of multivariate time series (MTS) data from various real-world systems. Anomaly detection in high-dimensional MTS data is essential to improving the productivity and safety of such systems; however, capturing the complex intercorrelations between different pairs of time series related to anomalous patterns is challenging. In this study, two different anomaly detection problems-mean shift and structural change-were defined based on the correlation dependency of MTS. Existing algorithms were experimentally analyzed and compared based on their correlation dependency encoding methods using synthetic datasets, with the results revealing that the explicit encoding of correlation dependency improves the predictive performance of anomaly detection in MTS data.

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Section: B Linear Gaussian Modelmentioning

confidence: 99%

Machine Learning-Based Anomaly Detection for Multivariate Time Series With Correlation Dependency

Chauhan

Lee

2022

IEEE Access

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“…Reference [26] introduced a multiscale method for changepoint detection that attains a trade-off between sensitivity and accuracy. In [27], structural changes are determined based on the difference between learned background precision matrix and sliding foreground precision matrix. Our approach is different as it uses description length for anomaly detection in data; that is, we encode data using a lossless source coder, and use the resulting codelength as the decision criteria.…”

Section: Previous Workmentioning

confidence: 99%

Graph Coding for Model Selection and Anomaly Detection in Gaussian Graphical Models

Abolfazli,

Host-Madsen,

Zhang

et al. 2021

Preprint

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A classic application of description length is for model selection with the minimum description length (MDL) principle. The focus of this paper is to extend description length for data analysis beyond simple model selection and sequences of scalars. More specifically, we extend the description length for data analysis in Gaussian graphical models. These are powerful tools to model interactions among variables in a sequence of i.i.d Gaussian data in the form of a graph. Our method uses universal graph coding methods to accurately account for model complexity, and therefore provide a more rigorous approach for graph model selection. The developed method is tested with synthetic and electrocardiogram (ECG) data to find the graph model and anomaly in Gaussian graphical models. The experiments show that our method gives better performance compared to commonly used methods.

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Graph Coding for Model Selection and Anomaly Detection in Gaussian Graphical Models

Abolfazli

Høst-Madsen

Zhang

et al. 2021

2021 IEEE International Symposium on Information Theory (ISIT)

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Contrastive Structured Anomaly Detection for Gaussian Graphical Models

Abstract: Gaussian graphical models (GGMs) are probabilistic tools of choice for analyzing conditional dependencies between variables in complex systems. Finding changepoints in the structural evo-

Cited by 4 publications

References 7 publications

Machine Learning-Based Anomaly Detection for Multivariate Time Series With Correlation Dependency

Machine Learning-Based Anomaly Detection for Multivariate Time Series With Correlation Dependency

Graph Coding for Model Selection and Anomaly Detection in Gaussian Graphical Models

Graph Coding for Model Selection and Anomaly Detection in Gaussian Graphical Models

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