24 citations · 72 across the 24 of their papers we have counts for
7 papers · 1 filter
Variable selection with genetic algorithms using repeated cross-validation of PLS regression models as fitness measure
David Kepplinger, Peter Filzmoser, Kurt Varmuza
Genetic algorithms are a widely used method in chemometrics for extracting variable subsets with high prediction power. Most fitness measures used by these genetic algorithms are b…
Clustering of imbalanced high-dimensional media data
Sarka Brodinova, Maia Zaharieva, Peter Filzmoser +2
Media content in large repositories usually exhibits multiple groups of strongly varying sizes. Media of potential interest often form notably smaller groups. Such media groups dif…
Robust and sparse k-means clustering for high-dimensional data
Sarka Brodinova, Peter Filzmoser, Thomas Ortner +2
In real-world application scenarios, the identification of groups poses a significant challenge due to possibly occurring outliers and existing noise variables. Therefore, there is…
Multigroup discrimination based on weighted local projections
Thomas Ortner, Irene Hoffmann, Peter Filzmoser +3
A novel approach for supervised classification analysis for high dimensional and flat data (more variables than observations) is proposed. We use the information of class-membershi…
Local projections for high-dimensional outlier detection
Thomas Ortner, Peter Filzmoser, Maia Zaharieva +2
In this paper, we propose a novel approach for outlier detection, called local projections, which is based on concepts of Local Outlier Factor (LOF) (Breunig et al., 2000) and RobP…
Robust and sparse estimation methods for high dimensional linear and logistic regression
Fatma Sevinc Kurnaz, Irene Hoffmann, Peter Filzmoser
Fully robust versions of the elastic net estimator are introduced for linear and logistic regression. The algorithms to compute the estimators are based on the idea of repeatedly a…
Guided projections for analysing the structure of high-dimensional data
Thomas Ortner, Peter Filzmoser, Maia Zaharieva +2
A powerful data transformation method named guided projections is proposed creating new possibilities to reveal the group structure of high-dimensional data in the presence of nois…