Abstract
Many problems in machine learning and imaging can be framed as an infinite
dimensional Lasso problem to estimate a sparse measure. This includes for
instance regression using a continuously parameterized dictionary, mixture
model estimation and super-resolution of images. To make the problem tractable,
one typically sketches the observations (often called compressive-sensing in
imaging) using randomized projections. In this work, we provide a comprehensive
treatment of the recovery performances of this class of approaches, proving
that (up to log factors) a number of sketches proportional to the sparsity is
enough to identify the sought after measure with robustness to noise. We prove
both exact support stability (the number of recovered atoms matches that of the
measure of interest) and approximate stability (localization of the atoms) by
extending two classical proof techniques (minimal norm dual certificate and
golfing scheme certificate).
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