Publication
Title
Real-time outlier detection for large datasets by RT-DetMCD
Author
Abstract
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.
Language
English
Source (journal)
Chemometrics and intelligent laboratory systems. - Amsterdam, 1986, currens
Publication
Amsterdam : 2020
ISSN
0169-7439 [print]
1873-3239 [online]
DOI
10.1016/J.CHEMOLAB.2020.103957
Volume/pages
199 (2020) , p. 1-10
Article Reference
103957
ISI
000527296200014
Full text (Publisher's DOI)
Full text (publisher's version - intranet only)
UAntwerpen
Research group
Publication type
Subject
External links
Web of Science
Record
Identifier
Creation 27.02.2024
Last edited 07.03.2024
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