52 citations · 192 across the 24 of their papers we have counts for
9 papers · 1 filter
Greedy sampling designs via reduced basis methods: optimal recovery in the uniform norm
Sebastian Neumayer, Kateryna Pozharska, Tino Ullrich
We study optimal sampling recovery in reproducing kernel Hilbert spaces (RKHS) in the uniform norm. For every RKHS with bounded kernel, we establish new comparisons between linear…
High-dimensional sparse recovery from function samples - Decoders, guarantees and instance optimality
Moritz Moeller, Sebastian Neumayer, Kateryna Pozharska +2
We investigate the reconstruction of multivariate functions from samples using sparse recovery techniques. For Square Root Lasso, Orthogonal Matching Pursuit, and Compressive Sampl…
Transfer Operators from Optimal Transport Plans for Coherent Set Detection
Péter Koltai, Johannes von Lindheim, Sebastian Neumayer +1
The topic of this study lies in the intersection of two fields. One is related with analyzing transport phenomena in complicated flows.For this purpose, we use so-called coherent s…
Parseval Proximal Neural Networks
Marzieh Hasannasab, Johannes Hertrich, Sebastian Neumayer +3
The aim of this paper is twofold. First, we show that a certain concatenation of a proximity operator with an affine operator is again a proximity operator on a suitable Hilbert sp…
Minimal Lipschitz and -Harmonic Extensions of Vector-Valued Functions on Finite Graphs
Miroslav Bačák, Johannes Hertrich, Sebastian Neumayer +1
This paper deals with extensions of vector-valued functions on finite graphs fulfilling distinguished minimality properties. We show that so-called lex and L-lex minimal extensions…
On the Robust PCA and Weiszfeld's Algorithm
Sebastian Neumayer, Max Nimmer, Simon Setzer +1
Principal component analysis (PCA) is a powerful standard tool for reducing the dimensionality of data. Unfortunately, it is sensitive to outliers so that various robust PCA varian…
On the Rotational Invariant -Norm PCA
Sebastian Neumayer, Max Nimmer, Simon Setzer +1
Principal component analysis (PCA) is a powerful tool for dimensionality reduction. Unfortunately, it is sensitive to outliers, so that various robust PCA variants were proposed in…
Regularization of Inverse Problems via Time Discrete Geodesics in Image Spaces
Sebastian Neumayer, Johannes Persch, Gabriele Steidl
This paper addresses the solution of inverse problems in imaging given an additional reference image. We combine a modification of the discrete geodesic path model for image metamo…
Morphing of Manifold-Valued Images inspired by Discrete Geodesics in Image Spaces
Sebastian Neumayer, Johannes Persch, Gabriele Steidl
This paper addresses the morphing of manifold-valued images based on the time discrete geodesic paths model of Berkels, Effland and Rumpf 2015. Although for our manifold-valued set…