121 citations · 197 across the 18 of their papers we have counts for
6 papers · 1 filter
DCSI -- An improved measure of cluster separability based on separation and connectedness
Jana Gauss, Fabian Scheipl, Moritz Herrmann
Whether class labels in a given data set correspond to meaningful clusters is crucial for the evaluation of clustering algorithms using real-world data sets. This property can be q…
A geometric framework for outlier detection in high-dimensional data
Moritz Herrmann, Florian Pfisterer, Fabian Scheipl
Outlier or anomaly detection is an important task in data analysis. We discuss the problem from a geometrical perspective and provide a framework that exploits the metric structure…
A geometric perspective on functional outlier detection
Moritz Herrmann, Fabian Scheipl
We consider functional outlier detection from a geometric perspective, specifically: for functional data sets drawn from a functional manifold which is defined by the data's modes…
Unsupervised Functional Data Analysis via Nonlinear Dimension Reduction
Moritz Herrmann, Fabian Scheipl
In recent years, manifold methods have moved into focus as tools for dimension reduction. Assuming that the high-dimensional data actually lie on or close to a low-dimensional nonl…
A General Machine Learning Framework for Survival Analysis
Andreas Bender, David Rügamer, Fabian Scheipl +1
The modeling of time-to-event data, also known as survival analysis, requires specialized methods that can deal with censoring and truncation, time-varying features and effects, an…
Benchmarking time series classification -- Functional data vs machine learning approaches
Florian Pfisterer, Laura Beggel, Xudong Sun +2
Time series classification problems have drawn increasing attention in the machine learning and statistical community. Closely related is the field of functional data analysis (FDA…