718 research outputs found
One-Bit Quantization Design and Adaptive Methods for Compressed Sensing
There have been a number of studies on sparse signal recovery from one-bit
quantized measurements. Nevertheless, little attention has been paid to the
choice of the quantization thresholds and its impact on the signal recovery
performance. This paper examines the problem of one-bit quantizer design for
sparse signal recovery. Our analysis shows that the magnitude ambiguity that
ever plagues conventional one-bit compressed sensing methods can be resolved,
and an arbitrarily small reconstruction error can be achieved by setting the
quantization thresholds close enough to the original data samples without being
quantized. Note that unquantized data samples are unaccessible in practice. To
overcome this difficulty, we propose an adaptive quantization method that
adaptively adjusts the quantization thresholds in a way such that the
thresholds converges to the optimal thresholds. Numerical results are
illustrated to collaborate our theoretical results and the effectiveness of the
proposed algorithm
A biconvex analysis for Lasso l1 reweighting
l1 reweighting algorithms are very popular in sparse signal recovery and
compressed sensing, since in the practice they have been observed to outperform
classical l1 methods. Nevertheless, the theoretical analysis of their
convergence is a critical point, and generally is limited to the convergence of
the functional to a local minimum or to subsequence convergence. In this
letter, we propose a new convergence analysis of a Lasso l1 reweighting method,
based on the observation that the algorithm is an alternated convex search for
a biconvex problem. Based on that, we are able to prove the numerical
convergence of the sequence of the iterates generated by the algorithm. This is
not yet the convergence of the sequence, but it is close enough for practical
and numerical purposes. Furthermore, we propose an alternative iterative soft
thresholding procedure, which is faster than the main algorithm
Application of Compressive Sensing Techniques in Distributed Sensor Networks: A Survey
In this survey paper, our goal is to discuss recent advances of compressive
sensing (CS) based solutions in wireless sensor networks (WSNs) including the
main ongoing/recent research efforts, challenges and research trends in this
area. In WSNs, CS based techniques are well motivated by not only the sparsity
prior observed in different forms but also by the requirement of efficient
in-network processing in terms of transmit power and communication bandwidth
even with nonsparse signals. In order to apply CS in a variety of WSN
applications efficiently, there are several factors to be considered beyond the
standard CS framework. We start the discussion with a brief introduction to the
theory of CS and then describe the motivational factors behind the potential
use of CS in WSN applications. Then, we identify three main areas along which
the standard CS framework is extended so that CS can be efficiently applied to
solve a variety of problems specific to WSNs. In particular, we emphasize on
the significance of extending the CS framework to (i). take communication
constraints into account while designing projection matrices and reconstruction
algorithms for signal reconstruction in centralized as well in decentralized
settings, (ii) solve a variety of inference problems such as detection,
classification and parameter estimation, with compressed data without signal
reconstruction and (iii) take practical communication aspects such as
measurement quantization, physical layer secrecy constraints, and imperfect
channel conditions into account. Finally, open research issues and challenges
are discussed in order to provide perspectives for future research directions
Super-Resolution From Binary Measurements With Unknown Threshold
We address the problem of super-resolution of point sources from binary
measurements, where random projections of the blurred measurement of the actual
signal are encoded using only the sign information. The threshold used for
binary quantization is not known to the decoder. We develop an algorithm that
solves convex programs iteratively and achieves signal recovery. The proposed
algorithm, which we refer to as the binary super-resolution (BSR) algorithm,
recovers point sources with reasonable accuracy, albeit up to a scale factor.
We show through simulations that the BSR algorithm is successful in recovering
the locations and the amplitudes of the point sources, even in the presence of
significant amount of blurring. We also propose a framework for handling noisy
measurements and demonstrate that BSR gives a reliable reconstruction
(correspondingly, reconstruction signal-to-noise ratio (SNR) of about 22 dB)
for a measurement SNR of 15 dB
Efficient iterative thresholding algorithms with functional feedbacks and convergence analysis
An accelerated class of adaptive scheme of iterative thresholding algorithms
is studied analytically and empirically. They are based on the feedback
mechanism of the null space tuning techniques (NST+HT+FB). The main
contribution of this article is the accelerated convergence analysis and proofs
with a variable/adaptive index selection and different feedback principles at
each iteration. These convergence analysis require no longer a priori sparsity
information of a signal. %key theory in this paper is the concept that the
number of indices selected at each iteration should be considered in order to
speed up the convergence. It is shown that uniform recovery of all -sparse
signals from given linear measurements can be achieved under reasonable
(preconditioned) restricted isometry conditions. Accelerated convergence rate
and improved convergence conditions are obtained by selecting an appropriate
size of the index support per iteration. The theoretical findings are
sufficiently demonstrated and confirmed by extensive numerical experiments. It
is also observed that the proposed algorithms have a clearly advantageous
balance of efficiency, adaptivity and accuracy compared with all other
state-of-the-art greedy iterative algorithms
Compressed Sensing for Wireless Communications : Useful Tips and Tricks
As a paradigm to recover the sparse signal from a small set of linear
measurements, compressed sensing (CS) has stimulated a great deal of interest
in recent years. In order to apply the CS techniques to wireless communication
systems, there are a number of things to know and also several issues to be
considered. However, it is not easy to come up with simple and easy answers to
the issues raised while carrying out research on CS. The main purpose of this
paper is to provide essential knowledge and useful tips that wireless
communication researchers need to know when designing CS-based wireless
systems. First, we present an overview of the CS technique, including basic
setup, sparse recovery algorithm, and performance guarantee. Then, we describe
three distinct subproblems of CS, viz., sparse estimation, support
identification, and sparse detection, with various wireless communication
applications. We also address main issues encountered in the design of CS-based
wireless communication systems. These include potentials and limitations of CS
techniques, useful tips that one should be aware of, subtle points that one
should pay attention to, and some prior knowledge to achieve better
performance. Our hope is that this article will be a useful guide for wireless
communication researchers and even non-experts to grasp the gist of CS
techniques
Nonlinear Residual Minimization by Iteratively Reweighted Least Squares
We address the numerical solution of minimal norm residuals of {\it
nonlinear} equations in finite dimensions. We take inspiration from the problem
of finding a sparse vector solution by using greedy algorithms based on
iterative residual minimizations in the -norm, for .
Due to the mild smoothness of the problem, especially for , we develop
and analyze a generalized version of Iteratively Reweighted Least Squares
(IRLS). This simple and efficient algorithm performs the solution of
optimization problems involving non-quadratic possibly non-convex and
non-smooth cost functions, which can be transformed into a sequence of common
least squares problems, which can be tackled more efficiently.While its
analysis has been developed in many contexts when the model equation is {\it
linear}, no results are provided in the {\it nonlinear} case. We address the
convergence and the rate of error decay of IRLS for nonlinear problems. The
convergence analysis is based on its reformulation as an alternating
minimization of an energy functional, whose variables are the competitors to
solutions of the intermediate reweighted least squares problems. Under specific
conditions of coercivity and local convexity, we are able to show convergence
of IRLS to minimizers of the nonlinear residual problem. For the case where we
are lacking local convexity, we propose an appropriate convexification.. To
illustrate the theoretical results we conclude the paper with several numerical
experiments. We compare IRLS with standard Matlab functions for an easily
presentable example and numerically validate our theoretical results in the
more complicated framework of phase retrieval problems. Finally we examine the
recovery capability of the algorithm in the context of data corrupted by
impulsive noise where the sparsification of the residual is desired.Comment: 37 pages. arXiv admin note: text overlap with arXiv:0807.0575 by
other author
Solving OSCAR regularization problems by proximal splitting algorithms
The OSCAR (octagonal selection and clustering algorithm for regression)
regularizer consists of a L_1 norm plus a pair-wise L_inf norm (responsible for
its grouping behavior) and was proposed to encourage group sparsity in
scenarios where the groups are a priori unknown. The OSCAR regularizer has a
non-trivial proximity operator, which limits its applicability. We reformulate
this regularizer as a weighted sorted L_1 norm, and propose its grouping
proximity operator (GPO) and approximate proximity operator (APO), thus making
state-of-the-art proximal splitting algorithms (PSAs) available to solve
inverse problems with OSCAR regularization. The GPO is in fact the APO followed
by additional grouping and averaging operations, which are costly in time and
storage, explaining the reason why algorithms with APO are much faster than
that with GPO. The convergences of PSAs with GPO are guaranteed since GPO is an
exact proximity operator. Although convergence of PSAs with APO is may not be
guaranteed, we have experimentally found that APO behaves similarly to GPO when
the regularization parameter of the pair-wise L_inf norm is set to an
appropriately small value. Experiments on recovery of group-sparse signals
(with unknown groups) show that PSAs with APO are very fast and accurate
From Bayesian Sparsity to Gated Recurrent Nets
The iterations of many first-order algorithms, when applied to minimizing
common regularized regression functions, often resemble neural network layers
with pre-specified weights. This observation has prompted the development of
learning-based approaches that purport to replace these iterations with
enhanced surrogates forged as DNN models from available training data. For
example, important NP-hard sparse estimation problems have recently benefitted
from this genre of upgrade, with simple feedforward or recurrent networks
ousting proximal gradient-based iterations. Analogously, this paper
demonstrates that more powerful Bayesian algorithms for promoting sparsity,
which rely on complex multi-loop majorization-minimization techniques, mirror
the structure of more sophisticated long short-term memory (LSTM) networks, or
alternative gated feedback networks previously designed for sequence
prediction. As part of this development, we examine the parallels between
latent variable trajectories operating across multiple time-scales during
optimization, and the activations within deep network structures designed to
adaptively model such characteristic sequences. The resulting insights lead to
a novel sparse estimation system that, when granted training data, can estimate
optimal solutions efficiently in regimes where other algorithms fail, including
practical direction-of-arrival (DOA) and 3D geometry recovery problems. The
underlying principles we expose are also suggestive of a learning process for a
richer class of multi-loop algorithms in other domains
Solving Almost all Systems of Random Quadratic Equations
This paper deals with finding an -dimensional solution to a system of
quadratic equations of the form for , which is also known as phase retrieval and is NP-hard in general. We put
forth a novel procedure for minimizing the amplitude-based least-squares
empirical loss, that starts with a weighted maximal correlation initialization
obtainable with a few power or Lanczos iterations, followed by successive
refinements based upon a sequence of iteratively reweighted (generalized)
gradient iterations. The two (both the initialization and gradient flow) stages
distinguish themselves from prior contributions by the inclusion of a fresh
(re)weighting regularization technique. The overall algorithm is conceptually
simple, numerically scalable, and easy-to-implement. For certain random
measurement models, the novel procedure is shown capable of finding the true
solution in time proportional to reading the data . This holds with high probability and without extra assumption on the
signal to be recovered, provided that the number of equations is some
constant times the number of unknowns in the signal vector, namely,
. Empirically, the upshots of this contribution are: i) (almost)
perfect signal recovery in the high-dimensional (say e.g., ) regime
given only an information-theoretic limit number of noiseless equations,
namely, in the real-valued Gaussian case; and, ii) (nearly) optimal
statistical accuracy in the presence of additive noise of bounded support.
Finally, substantial numerical tests using both synthetic data and real images
corroborate markedly improved signal recovery performance and computational
efficiency of our novel procedure relative to state-of-the-art approaches.Comment: 27 pages, 8 figure
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