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Title
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Real-time outlier detection for large datasets by RT-DetMCD
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Author
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Abstract
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| Modern industrial machines can generate gigabytes of data in seconds, frequently pushing the boundaries of available computing power. Together with the time criticality of industrial processing this presents a challenging problem for any data analytics procedure. We focus on the deterministic minimum covariance determinant method (DetMCD), which detects outliers by fitting a robust covariance matrix. We construct a much faster version of DetMCD by replacing its initial estimators by two new methods and incorporating update-based concentration steps. The computation time is reduced further by parallel computing, with a novel robust aggregation method to combine the results from the threads. The speed and accuracy of the proposed real-time DetMCD method (RT-DetMCD) are illustrated by simulation and a real industrial application to food sorting. |
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Language
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English
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Source (journal)
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Chemometrics and intelligent laboratory systems. - Amsterdam, 1986, currens
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Publication
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Amsterdam
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2020
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ISSN
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0169-7439
[print]
1873-3239
[online]
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DOI
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10.1016/J.CHEMOLAB.2020.103957
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Volume/pages
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199
(2020)
, p. 1-10
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Article Reference
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103957
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ISI
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000527296200014
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Medium
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E-only publicatie
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Full text (Publisher's DOI)
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Full text (publisher's version - intranet only)
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