46 research outputs found
Krylov Methods for Low-Rank Regularization
This paper introduces new solvers for the computation of low-rank approximate
solutions to large-scale linear problems, with a particular focus on the
regularization of linear inverse problems. Although Krylov methods
incorporating explicit projections onto low-rank subspaces are already used for
well-posed systems that arise from discretizing stochastic or time-dependent
PDEs, we are mainly concerned with algorithms that solve the so-called nuclear
norm regularized problem, where a suitable nuclear norm penalization on the
solution is imposed alongside a fit-to-data term expressed in the 2-norm: this
has the effect of implicitly enforcing low-rank solutions. By adopting an
iteratively reweighted norm approach, the nuclear norm regularized problem is
reformulated as a sequence of quadratic problems, which can then be efficiently
solved using Krylov methods, giving rise to an inner-outer iteration scheme.
Our approach differs from the other solvers available in the literature in
that: (a) Kronecker product properties are exploited to define the reweighted
2-norm penalization terms; (b) efficient preconditioned Krylov methods replace
gradient (projection) methods; (c) the regularization parameter can be
efficiently and adaptively set along the iterations. Furthermore, we
reformulate within the framework of flexible Krylov methods both the new
inner-outer methods for nuclear norm regularization and some of the existing
Krylov methods incorporating low-rank projections. This results in an even more
computationally efficient (but heuristic) strategy, that does not rely on an
inner-outer iteration scheme. Numerical experiments show that our new solvers
are competitive with other state-of-the-art solvers for low-rank problems, and
deliver reconstructions of increased quality with respect to other classical
Krylov methods
Accurate tensor completion via adaptive low-rank representation
Date of publication December 30, 2019; date of current version October 6, 2020Low-rank representation-based approaches that assume low-rank tensors and exploit their low-rank structure with appropriate prior models have underpinned much of the recent progress in tensor completion. However, real tensor data only approximately comply with the low-rank requirement in most cases, viz., the tensor consists of low-rank (e.g., principle part) as well as non-low-rank (e.g., details) structures, which limit the completion accuracy of these approaches. To address this problem, we propose an adaptive low-rank representation model for tensor completion that represents low-rank and non-low-rank structures of a latent tensor separately in a Bayesian framework. Specifically, we reformulate the CANDECOMP/PARAFAC (CP) tensor rank and develop a sparsity-induced prior for the low-rank structure that can be used to determine tensor rank automatically. Then, the non-low-rank structure is modeled using a mixture of Gaussians prior that is shown to be sufficiently flexible and powerful to inform the completion process for a variety of real tensor data. With these two priors, we develop a Bayesian minimum mean-squared error estimate framework for inference. The developed framework can capture the important distinctions between low-rank and non-low-rank structures, thereby enabling more accurate model, and ultimately, completion. For various applications, compared with the state-of-the-art methods, the proposed model yields more accurate completion results.Lei Zhang, Wei Wei, Qinfeng Shi, Chunhua Shen, Anton van den Hengel, and Yanning Zhan
A Theoretically Guaranteed Quaternion Weighted Schatten p-norm Minimization Method for Color Image Restoration
Inspired by the fact that the matrix formulated by nonlocal similar patches
in a natural image is of low rank, the rank approximation issue have been
extensively investigated over the past decades, among which weighted nuclear
norm minimization (WNNM) and weighted Schatten -norm minimization (WSNM) are
two prevailing methods have shown great superiority in various image
restoration (IR) problems. Due to the physical characteristic of color images,
color image restoration (CIR) is often a much more difficult task than its
grayscale image counterpart. However, when applied to CIR, the traditional
WNNM/WSNM method only processes three color channels individually and fails to
consider their cross-channel correlations. Very recently, a quaternion-based
WNNM approach (QWNNM) has been developed to mitigate this issue, which is
capable of representing the color image as a whole in the quaternion domain and
preserving the inherent correlation among the three color channels. Despite its
empirical success, unfortunately, the convergence behavior of QWNNM has not
been strictly studied yet. In this paper, on the one side, we extend the WSNM
into quaternion domain and correspondingly propose a novel quaternion-based
WSNM model (QWSNM) for tackling the CIR problems. Extensive experiments on two
representative CIR tasks, including color image denoising and deblurring,
demonstrate that the proposed QWSNM method performs favorably against many
state-of-the-art alternatives, in both quantitative and qualitative
evaluations. On the other side, more importantly, we preliminarily provide a
theoretical convergence analysis, that is, by modifying the quaternion
alternating direction method of multipliers (QADMM) through a simple
continuation strategy, we theoretically prove that both the solution sequences
generated by the QWNNM and QWSNM have fixed-point convergence guarantees.Comment: 46 pages, 10 figures; references adde