O-LoMST: An Online Anomaly Detection Approach And Its Application In A Hydropower Generation Plant

Ahmed, Imtiaz; Galoppo, Travis; Ding, Yu

doi:10.1109/coase.2019.8843317

Cited by 4 publications

(1 citation statement)

References 21 publications

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“…We use both online and offline version of NS-NMF. To detect anomalies on the fly, we use the threshold update policy similar to (Ahmed et al, 2019b) for the online algorithm. To save space we skip some rows in the table.…”

Section: Application To a Power Plant Examplementioning

confidence: 99%

Neighborhood Structure Assisted Non-negative Matrix Factorization and its Application in Unsupervised Point-wise Anomaly Detection

Ahmed,

Hu,

Acharya

et al. 2020

Preprint

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Dimensionality reduction is considered as an important step for ensuring competitive performance in unsupervised learning such as anomaly detection. Non-negative matrix factorization (NMF) is a popular and widely used method to accomplish this goal. But NMF, together with its recent, enhanced version, like graph regularized NMF or symmetric NMF, do not have the provision to include the neighborhood structure information and, as a result, may fail to provide satisfactory performance in presence of nonlinear manifold structure. To address that shortcoming, we propose to consider and incorporate the neighborhood structural similarity information within the NMF framework by modeling the data through a minimum spanning tree. What motivates our choice is the understanding that in the presence of complicated data structure, a minimum spanning tree can approximate the intrinsic distance between two data points better than a simple Euclidean distance does, and consequently, it constitutes a more reasonable basis for differentiating anomalies from the normal class data. We label the resulting method as the neighborhood structure assisted NMF. By comparing the formulation and properties of the neighborhood structure assisted NMF with other versions of NMF including graph regularized NMF and symmetric NMF, it is apparent that the inclusion of the neighborhood structure information using minimum spanning tree makes a key difference. We further devise both offline and online algorithmic versions of the proposed method. Empirical comparisons using twenty benchmark datasets as well as an industrial dataset extracted from a hydropower plant 1

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Section: Application To a Power Plant Examplementioning

confidence: 99%