Deep connections exist between harmonic and applied analysis and
the diverse yet connected topics of machine learning, data
analysis, and imaging science. This volume explores these rapidly
growing areas and features contributions presented at the second
and third editions of the Summer Schools on Applied Harmonic
Analysis, held at the University of Genova in 2017 and 2019. Each
chapter offers an introduction to essential material and then
demonstrates connections to more advanced research, with the aim of
providing an accessible entrance for students and researchers.
Topics covered include ill-posed problems; concentration
inequalities; regularization and large-scale machine learning;
unitarization of the radon transform on symmetric spaces; and
proximal gradient methods for machine learning and imaging.
General
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