5 papers
Optimal Reaction Coordinates: Variational Characterization and Sparse Computation
Andreas Bittracher, Mattes Mollenhauer, Péter Koltai +1
Reaction Coordinates (RCs) are indicators of hidden, low-dimensional mechanisms that govern the long-term behavior of high-dimensional stochastic processes. We present a novel and…
Kernel Conditional Density Operators
Ingmar Schuster, Mattes Mollenhauer, Stefan Klus +1
We introduce a novel conditional density estimation model termed the conditional density operator (CDO). It naturally captures multivariate, multimodal output densities and shows p…
Kernel methods for detecting coherent structures in dynamical data
Stefan Klus, Brooke E. Husic, Mattes Mollenhauer +1
We illustrate relationships between classical kernel-based dimensionality reduction techniques and eigendecompositions of empirical estimates of reproducing kernel Hilbert space (R…
Singular Value Decomposition of Operators on Reproducing Kernel Hilbert Spaces
Mattes Mollenhauer, Ingmar Schuster, Stefan Klus +1
Reproducing kernel Hilbert spaces (RKHSs) play an important role in many statistics and machine learning applications ranging from support vector machines to Gaussian processes and…
On Hyperparameter Search in Cluster Ensembles
Luzie Helfmann, Johannes von Lindheim, Mattes Mollenhauer +1
Quality assessments of models in unsupervised learning and clustering verification in particular have been a long-standing problem in the machine learning research. The lack of rob…