activity
20192022
most citedESPRIT versus ESPIRA for reconstruction of short cosine sums and its application

1 citations · 1 across the 4 of their papers we have counts for

collaborators

6 papers

math.NA20221 cited

ESPRIT versus ESPIRA for reconstruction of short cosine sums and its application

Nadiia Derevianko, Gerlind Plonka, Raha Razavi

In this paper we introduce two new algorithms for stable approximation with and recovery of short cosine sums. The used signal model contains cosine terms with arbitrary real posit…

math.NA2021

Exact Reconstruction of Extended Exponential Sums using Rational Approximation of their Fourier Coefficients

Nadiia Derevianko, Gerlind Plonka

In this paper we derive a new recovery procedure for the reconstruction of extended exponential sums of the form $y(t) = \sum_{j=1}^{M} \left( \sum_{m=0}^{n_j} \, γ_{j,m} \, t^{m}…

math.NA2020

Exact Reconstruction of Sparse Non-Harmonic Signals from Fourier Coefficients

Markus Petz, Gerlind Plonka, Nadiia Derevianko

In this paper, we derive a new reconstruction method for real non-harmonic Fourier sums, i.e., real signals which can be represented as sparse exponential sums of the form $f(t) =…

math.NA2020

Optimal Rank-1 Hankel Approximation of Matrices: Frobenius Norm, Spectral Norm, and Cadzow's Algorithm

Hanna Knirsch, Markus Petz, Gerlind Plonka

We characterize optimal rank-1 matrix approximations with Hankel or Toeplitz structure with regard to two different norms, the Frobenius norm and the spectral norm, in a new way. M…

math.NA2020

Modifications of Prony's Method for the Recovery and Sparse Approximation of Generalized Exponential Sums

Ingeborg Keller, Gerlind Plonka

In this survey we describe some modifications of Prony's method. In particular, we consider the recovery of general expansions into eigenfunctions of linear differential operators…

cs.LG2019

A Tree-based Dictionary Learning Framework

Renato Budinich, Gerlind Plonka

We propose a new outline for adaptive dictionary learning methods for sparse encoding based on a hierarchical clustering of the training data. Through recursive application of a cl…