125 citations · 136 across the 6 of their papers we have counts for
7 papers
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…
Enhancing cluster analysis via topological manifold learning
Moritz Herrmann, Daniyal Kazempour, Fabian Scheipl +1
We discuss topological aspects of cluster analysis and show that inferring the topological structure of a dataset before clustering it can considerably enhance cluster detection: t…
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…
Over-optimism in benchmark studies and the multiplicity of design and analysis options when interpreting their results
Christina Nießl, Moritz Herrmann, Chiara Wiedemann +2
In recent years, the need for neutral benchmark studies that focus on the comparison of methods from computational sciences has been increasingly recognised by the scientific commu…
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…
Large-scale benchmark study of survival prediction methods using multi-omics data
Moritz Herrmann, Philipp Probst, Roman Hornung +2
Multi-omics data, that is, datasets containing different types of high-dimensional molecular variables (often in addition to classical clinical variables), are increasingly generat…