95 citations · 768 across the 58 of their papers we have counts for
7 papers · 1 filter
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…
Frame Soft Shrinkage as Proximity Operator
Marzieh Hassanasab, Sebastian Neumayer, Gerlind Plonka +3
Let and be real Hilbert spaces and an injective operator with closed range and Moore-Penrose inverse $T^\dagge…
Alternatives to the EM Algorithm for ML-Estimation of Location, Scatter Matrix and Degree of Freedom of the Student- Distribution
Marzieh Hasannasab, Johannes Hertrich, Friederike Laus +1
In this paper, we consider maximum likelihood estimations of the degree of freedom parameter , the location parameter and the scatter matrix of the multivariate Student-…
Curve Based Approximation of Measures on Manifolds by Discrepancy Minimization
Martin Ehler, Manuel Gräf, Sebastian Neumayer +1
The approximation of probability measures on compact metric spaces and in particular on Riemannian manifoldsby atomic or empirical ones is a classical task in approximation and com…
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…