6 papers · 1 filter
Greedy techniques for inverse problems
L. Bruni Bruno, P. Massa, E. Perracchione +1
Inverse imaging problems rely on limited and indirect measurements, making reconstruction highly dependent on both regularization and sample locations. We introduce a novel greedy…
Feature Understanding and Sparsity Enhancement via 2-Layered kernel machines (2L-FUSE)
Fabiana Camattari, Sabrina Guastavino, Francesco Marchetti +1
We propose a novel sparsity enhancement strategy for regression tasks, based on learning a data-adaptive kernel metric, i.e., a shape matrix, through 2-Layered kernel machines. The…
Variably Scaled Kernels for the regularized solution of the parametric Fourier imaging problem
Anna Volpara, Alessandro Lupoli, Emma Perracchione
We address the problem of approximating parametric Fourier imaging problems via interpolation/ extrapolation algorithms that impose smoothing constraints across contiguous values o…
Efficient Reduced Basis Algorithm (ERBA) for kernel-based approximation
Francesco Marchetti, Emma Perracchione
The main purpose of this work is the one of providing an efficient scheme for constructing reduced interpolation models for kernel bases. In literature such problem is mainly addre…
Feature augmentation for the inversion of the Fourier transform with limited data
Emma Perracchione, Anna Maria Massone, Michele Piana
We investigate an interpolation/extrapolation method that, given scattered observations of the Fourier transform, approximates its inverse. The interpolation algorithm takes advant…
Data-driven extrapolation via feature augmentation based on variably scaled thin plate splines
Rosanna Campagna, Emma Perracchione
The data driven extrapolation requires the definition of a functional model depending on the available data and has the application scope of providing reliable predictions on the u…