Model selection with low complexity priors

S. Vaiter, M. Golbabaee, J. Fadili, G. Peyre

Research output: Contribution to journalArticlepeer-review

20 Citations (SciVal)


Regularization plays a pivotal role when facing the challenge of solving ill-posed inverse problems, where the number of observations is smaller than the ambient dimension of the object to be estimated. A line of recent work has studied regularization models with various types of low-dimensional structures. In such settings, the general approach is to solve a regularized optimization problem, which combines a data fidelity term and some regularization penalty that promotes the assumed low-dimensional/simple structure. This paper provides a general framework to capture this low-dimensional structure using what we call partly smooth functions relative to a linear manifold. These are convex, non-negative, closed and finite-valued functions that will promote objects living on low-dimensional subspaces. This class of regularizers encompasses many popular examples such as the ℓ1ℓ1-norm, ℓ1−ℓ2ℓ1−ℓ2-norm (group sparsity), as well as several others including the ℓ∞ℓ∞ norm. We also show that the set of partly smooth functions relative to a linear manifold is closed under addition and pre-composition by a linear operator, which allows us to cover mixed regularization, and the so-called analysis-type priors (e.g. total variation, fused Lasso, finite-valued polyhedral gauges). Our main result presents a unified sharp analysis of exact and robust recovery of the low-dimensional subspace model associated to the object to recover from partial measurements. This analysis is illustrated on a number of special and previously studied cases, and on an analysis of the performance of ℓ∞ℓ∞ regularization in a compressed sensing scenario.
Original languageEnglish
Pages (from-to)230–287
Number of pages58
JournalInformation and Inference
Issue number3
Publication statusPublished - 13 Apr 2015


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