Scattering transforms of sparse signals (A02)

• Subject Area: Mathematics
• Term: since 2022
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 442047500

The scattering transform is based on a designed convolutional neural network using a wavelet filter bank structure. Other filter banks have been proposed as basis of the scattering transform, but the impact of this choice is currently unclear. This project investigates the relevance of signal sparsity for the analysis of the scattering transform, with the aim of assessing the potential of other filter banks in this context. The fundamental hypothesis that we intend to investigate is that unstructured sparsity is generally not well-suited to describe relevant features of the response of scattering transforms, but that structured sparsity is.

• DFG Programme: Collaborative Research Centres
• Subproject of: SFB 1481: Sparsity and Singular Structures
• Applicant Institution: Rheinisch-Westfälische Technische Hochschule Aachen
• Project Head: Professor Dr. Hartmut Führ