3 citations · 3 across the 2 of their papers we have counts for
5 papers
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…
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…