255 citations · 898 across the 12 of their papers we have counts for
13 papers
Real-time discriminant analysis in the presence of label and measurement noise
Iwein Vranckx, Jakob Raymaekers, Bart De Ketelaere +2
Quadratic discriminant analysis (QDA) is a widely used classification technique. Based on a training dataset, each class in the data is characterized by an estimate of its center a…
Outlier detection in non-elliptical data by kernel MRCD
Joachim Schreurs, Iwein Vranckx, Mia Hubert +2
The minimum regularized covariance determinant method (MRCD) is a robust estimator for multivariate location and scatter, which detects outliers by fitting a robust covariance matr…
Class maps for visualizing classification results
Jakob Raymaekers, Peter J. Rousseeuw, Mia Hubert
Classification is a major tool of statistics and machine learning. A classification method first processes a training set of objects with given classes (labels), with the goal of a…
Real-time outlier detection for large datasets by RT-DetMCD
Bart De Ketelaere, Mia Hubert, Jakob Raymaekers +2
Modern industrial machines can generate gigabytes of data in seconds, frequently pushing the boundaries of available computing power. Together with the time criticality of industri…
MacroPCA: An all-in-one PCA method allowing for missing values as well as cellwise and rowwise outliers
Mia Hubert, Peter J. Rousseeuw, Wannes Van den Bossche
Multivariate data are typically represented by a rectangular matrix (table) in which the rows are the objects (cases) and the columns are the variables (measurements). When there a…
Minimum Covariance Determinant and Extensions
Mia Hubert, Michiel Debruyne, Peter J. Rousseeuw
The Minimum Covariance Determinant (MCD) method is a highly robust estimator of multivariate location and scatter, for which a fast algorithm is available. Since estimating the cov…