A Unifying Representer Theorem for Inverse Problems and Machine Learning

Regularization addresses the ill-posedness of the training problem in machine learning or the reconstruction of a signal from a limited number of measurements. The method is applicable whenever the problem is formulated as an optimization task. The standard strategy consists in augmenting the origin...

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Bibliographic Details
Published in:	Foundations of computational mathematics Vol. 21; no. 4; pp. 941 - 960
Main Author:	Unser, Michael
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.08.2021 Springer Nature B.V
Subjects:	Applications of Mathematics Banach spaces Computer Science Economics Inverse problems Linear and Multilinear Algebras Machine learning Math Applications in Computer Science Mathematics Mathematics and Statistics Matrix Theory Numerical Analysis Optimization Regularization Theorems 68T05 Convex optimization Machine learning Representer theorem 65J20 Banach space Regularization 46N10 47A52 Inverse problem
ISSN:	1615-3375, 1615-3383
Online Access:	Get full text
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