Learning the model from the data

• Carlos Cabrelli ^[1] ; Úrsula Molter ^[1]
  1. [1] Universidad de Buenos Aires

     Universidad de Buenos Aires

     Argentina

     
• Localización: Revista de la Unión Matemática Argentina, ISSN 0041-6932, ISSN-e 1669-9637, Vol. 66, Nº. 1, 2023, págs. 141-152
• Idioma: inglés
• DOI: 10.33044/revuma.4371
• Enlaces
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• Resumen

  • The task of approximating data with a concise model comprising only a few parameters is a key concern in many applications, particularly in signal processing. These models, typically subspaces belonging to a specific class, are carefully chosen based on the data at hand. In this survey, we review the latest research on data approximation using models with few parameters, with a specific emphasis on scenarios where the data is situated in finite-dimensional vector spaces, functional spaces such as L2(Rd), and other general situations. We highlight the invariant properties of these subspace-based models that make them suitable for diverse applications, particularly in the field of image processing.