83,012 research outputs found
Low rank matrix recovery from rank one measurements
We study the recovery of Hermitian low rank matrices from undersampled measurements via nuclear norm minimization. We
consider the particular scenario where the measurements are Frobenius inner
products with random rank-one matrices of the form for some
measurement vectors , i.e., the measurements are given by . The case where the matrix to be recovered
is of rank one reduces to the problem of phaseless estimation (from
measurements, via the PhaseLift approach,
which has been introduced recently. We derive bounds for the number of
measurements that guarantee successful uniform recovery of Hermitian rank
matrices, either for the vectors , , being chosen independently
at random according to a standard Gaussian distribution, or being sampled
independently from an (approximate) complex projective -design with .
In the Gaussian case, we require measurements, while in the case
of -designs we need . Our results are uniform in the
sense that one random choice of the measurement vectors guarantees
recovery of all rank -matrices simultaneously with high probability.
Moreover, we prove robustness of recovery under perturbation of the
measurements by noise. The result for approximate -designs generalizes and
improves a recent bound on phase retrieval due to Gross, Kueng and Krahmer. In
addition, it has applications in quantum state tomography. Our proofs employ
the so-called bowling scheme which is based on recent ideas by Mendelson and
Koltchinskii.Comment: 24 page
Unicity conditions for low-rank matrix recovery
Low-rank matrix recovery addresses the problem of recovering an unknown
low-rank matrix from few linear measurements. Nuclear-norm minimization is a
tractible approach with a recent surge of strong theoretical backing. Analagous
to the theory of compressed sensing, these results have required random
measurements. For example, m >= Cnr Gaussian measurements are sufficient to
recover any rank-r n x n matrix with high probability. In this paper we address
the theoretical question of how many measurements are needed via any method
whatsoever --- tractible or not. We show that for a family of random
measurement ensembles, m >= 4nr - 4r^2 measurements are sufficient to guarantee
that no rank-2r matrix lies in the null space of the measurement operator with
probability one. This is a necessary and sufficient condition to ensure uniform
recovery of all rank-r matrices by rank minimization. Furthermore, this value
of precisely matches the dimension of the manifold of all rank-2r matrices.
We also prove that for a fixed rank-r matrix, m >= 2nr - r^2 + 1 random
measurements are enough to guarantee recovery using rank minimization. These
results give a benchmark to which we may compare the efficacy of nuclear-norm
minimization
Uniqueness Conditions For Low-Rank Matrix Recovery
Low-rank matrix recovery addresses the problem of recovering an unknown low-rank matrix from few linear measurements. Nuclear-norm minimization is a tractible approach with a recent surge of strong theoretical backing. Analagous to the theory of compressed sensing, these results have required random measurements. For example, m \u3e= Cnr Gaussian measurements are sufficient to recover any rank-r n x n matrix with high probability. In this paper we address the theoretical question of how many measurements are needed via any method whatsoever --- tractible or not. We show that for a family of random measurement ensembles, m \u3e= 4nr - 4r^2 measurements are sufficient to guarantee that no rank-2r matrix lies in the null space of the measurement operator with probability one. This is a necessary and sufficient condition to ensure uniform recovery of all rank-r matrices by rank minimization. Furthermore, this value of m precisely matches the dimension of the manifold of all rank-2r matrices. We also prove that for a fixed rank-r matrix, m \u3e= 2nr - r^2 + 1 random measurements are enough to guarantee recovery using rank minimization. These results give a benchmark to which we may compare the efficacy of nuclear-norm minimization
Simultaneously Structured Models with Application to Sparse and Low-rank Matrices
The topic of recovery of a structured model given a small number of linear
observations has been well-studied in recent years. Examples include recovering
sparse or group-sparse vectors, low-rank matrices, and the sum of sparse and
low-rank matrices, among others. In various applications in signal processing
and machine learning, the model of interest is known to be structured in
several ways at the same time, for example, a matrix that is simultaneously
sparse and low-rank.
Often norms that promote each individual structure are known, and allow for
recovery using an order-wise optimal number of measurements (e.g.,
norm for sparsity, nuclear norm for matrix rank). Hence, it is reasonable to
minimize a combination of such norms. We show that, surprisingly, if we use
multi-objective optimization with these norms, then we can do no better,
order-wise, than an algorithm that exploits only one of the present structures.
This result suggests that to fully exploit the multiple structures, we need an
entirely new convex relaxation, i.e. not one that is a function of the convex
relaxations used for each structure. We then specialize our results to the case
of sparse and low-rank matrices. We show that a nonconvex formulation of the
problem can recover the model from very few measurements, which is on the order
of the degrees of freedom of the matrix, whereas the convex problem obtained
from a combination of the and nuclear norms requires many more
measurements. This proves an order-wise gap between the performance of the
convex and nonconvex recovery problems in this case. Our framework applies to
arbitrary structure-inducing norms as well as to a wide range of measurement
ensembles. This allows us to give performance bounds for problems such as
sparse phase retrieval and low-rank tensor completion.Comment: 38 pages, 9 figure
Compressed sensing and robust recovery of low rank matrices
In this paper, we focus on compressed sensing and recovery schemes for low-rank matrices, asking under what conditions a low-rank matrix can be sensed and recovered from incomplete, inaccurate, and noisy observations. We consider three schemes, one based on a certain Restricted Isometry Property and two based on directly sensing the row and column space of the matrix. We study their properties in terms of exact recovery in the ideal case, and robustness issues for approximately low-rank matrices and for noisy measurements
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