no code implementations • 3 May 2024 • Moritz Herrmann, F. Julian D. Lange, Katharina Eggensperger, Giuseppe Casalicchio, Marcel Wever, Matthias Feurer, David Rügamer, Eyke Hüllermeier, Anne-Laure Boulesteix, Bernd Bischl
We warn against a common but incomplete understanding of empirical research in machine learning (ML) that leads to non-replicable results, makes findings unreliable, and threatens to undermine progress in the field.
1 code implementation • 19 Oct 2023 • Jana Gauss, Fabian Scheipl, Moritz Herrmann
Detailed evaluation on frequently used real-world data sets shows that DCSI can correctly identify touching or overlapping classes that do not form meaningful clusters.
1 code implementation • 1 Jul 2022 • Moritz Herrmann, Daniyal Kazempour, Fabian Scheipl, Peer Kröger
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: theoretical arguments and empirical evidence show that clustering embedding vectors, representing the structure of a data manifold instead of the observed feature vectors themselves, is highly beneficial.
no code implementations • 1 Jul 2022 • Moritz Herrmann, Florian Pfisterer, Fabian Scheipl
Outlier or anomaly detection is an important task in data analysis.
1 code implementation • 14 Sep 2021 • 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 of variation in amplitude and phase.
1 code implementation • 22 Dec 2020 • Moritz Herrmann, Fabian Scheipl
The contributions of the paper are three-fold: First of all, we define a theoretical framework which allows to systematically assess specific challenges that arise in the functional data context, transfer several nonlinear dimension reduction methods for tabular and image data to functional data, and show that manifold methods can be used successfully in this setting.
1 code implementation • 7 Mar 2020 • Moritz Herrmann, Philipp Probst, Roman Hornung, Vindi Jurinovic, Anne-Laure Boulesteix
The Kaplan-Meier estimate and a Cox model using only clinical variables were used as reference methods.