INVESTIGADORES
CABRELLI Carlos Alberto
artículos
Título:
Learning optimal smooth invariant subspaces for data approximation
Autor/es:
BARBIERI, D.; CABRELLI, C.; HERNÁNDEZ, E.; MOLTER, U.
Revista:
JOURNAL OF MATHEMATICAL ANALYSIS AND APPLICATIONS
Editorial:
ACADEMIC PRESS INC ELSEVIER SCIENCE
Referencias:
Año: 2024
ISSN:
0022-247X
Resumen:
In this article, we consider the problem of approximating a finite set of data (usually huge in applications) by invariant subspaces generated by a small set of smooth functions. The invariance is either by translations under a full-rank lattice or through the action of crystallographic groups. Smoothness is ensured by stipulating that the generators belong to a Paley-Wiener space, which is selected in an optimal way based on the characteristics of the given data. To complete our investigation, we analyze the fundamental role played by the lattice in the process of approximation.
