954 research outputs found
Low Complexity Regularization of Linear Inverse Problems
Inverse problems and regularization theory is a central theme in contemporary
signal processing, where the goal is to reconstruct an unknown signal from
partial indirect, and possibly noisy, measurements of it. A now standard method
for recovering the unknown signal is to solve a convex optimization problem
that enforces some prior knowledge about its structure. This has proved
efficient in many problems routinely encountered in imaging sciences,
statistics and machine learning. This chapter delivers a review of recent
advances in the field where the regularization prior promotes solutions
conforming to some notion of simplicity/low-complexity. These priors encompass
as popular examples sparsity and group sparsity (to capture the compressibility
of natural signals and images), total variation and analysis sparsity (to
promote piecewise regularity), and low-rank (as natural extension of sparsity
to matrix-valued data). Our aim is to provide a unified treatment of all these
regularizations under a single umbrella, namely the theory of partial
smoothness. This framework is very general and accommodates all low-complexity
regularizers just mentioned, as well as many others. Partial smoothness turns
out to be the canonical way to encode low-dimensional models that can be linear
spaces or more general smooth manifolds. This review is intended to serve as a
one stop shop toward the understanding of the theoretical properties of the
so-regularized solutions. It covers a large spectrum including: (i) recovery
guarantees and stability to noise, both in terms of -stability and
model (manifold) identification; (ii) sensitivity analysis to perturbations of
the parameters involved (in particular the observations), with applications to
unbiased risk estimation ; (iii) convergence properties of the forward-backward
proximal splitting scheme, that is particularly well suited to solve the
corresponding large-scale regularized optimization problem
Fast algorithms for smooth and monotone covariance matrix estimation
In this thesis the problem of interest is, within the setting of financial risk management, covariance matrix estimation from limited number of high dimensional independent identically distributed (i.i.d.) multivariate samples when the random variables of interest have a natural spatial indexing along a low-dimensional manifold, e.g., along a line. Sample covariance matrix estimate is fraught with peril in this context. A variety of approaches to improve the covariance estimates have been developed by exploiting knowledge of structure in the data, which, however, in general impose very strict structure. We instead exploit another formulation which assumes that the covariance matrix is smooth and monotone with respect to the spatial indexing. Originally the formulation is derived from the estimation problem within a convex-optimization framework, and the resulting semidefinite-programming problem (SDP) is solved by an interior-point method (IPM). However, solving SDP via an IPM can become unduly computationally expensive for large covariance matrices. Motivated by this observation, this thesis develops highly efficient first-order solvers for smooth and monotone covariance matrix estimation. We propose two types of solvers for covariance matrix estimation: first based on projected gradients, and then based on recently developed optimal first order methods. Given such numerical algorithms, we present a comprehensive experimental analysis. We first demonstrate the benefits of imposing smoothness and monotonicity constraints in covariance matrix estimation in a number of scenarios, involving limited, missing, and asynchronous data. We then demonstrate the potential computational benefits offered by first order methods through a detailed comparison to solution of the problem via IPMs
Frontiers in Nonparametric Statistics
The goal of this workshop was to discuss recent developments of nonparametric statistical inference. A particular focus was on high dimensional statistics, semiparametrics, adaptation, nonparametric bayesian statistics, shape constraint estimation and statistical inverse problems. The close interaction of these issues with optimization, machine learning and inverse problems has been addressed as well
A General Framework for Fast Stagewise Algorithms
Forward stagewise regression follows a very simple strategy for constructing
a sequence of sparse regression estimates: it starts with all coefficients
equal to zero, and iteratively updates the coefficient (by a small amount
) of the variable that achieves the maximal absolute inner product
with the current residual. This procedure has an interesting connection to the
lasso: under some conditions, it is known that the sequence of forward
stagewise estimates exactly coincides with the lasso path, as the step size
goes to zero. Furthermore, essentially the same equivalence holds
outside of least squares regression, with the minimization of a differentiable
convex loss function subject to an norm constraint (the stagewise
algorithm now updates the coefficient corresponding to the maximal absolute
component of the gradient).
Even when they do not match their -constrained analogues, stagewise
estimates provide a useful approximation, and are computationally appealing.
Their success in sparse modeling motivates the question: can a simple,
effective strategy like forward stagewise be applied more broadly in other
regularization settings, beyond the norm and sparsity? The current
paper is an attempt to do just this. We present a general framework for
stagewise estimation, which yields fast algorithms for problems such as
group-structured learning, matrix completion, image denoising, and more.Comment: 56 pages, 15 figure
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