A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Filters
Support Recovery of Sparse Signals from a Mixture of Linear Measurements
[article]
2021
arXiv
pre-print
Preserved Fulltext
Other Versions
Recovery of support of a sparse vector from simple measurements is a widely-studied problem, considered under the frameworks of compressed sensing, 1-bit compressed sensing, and more general single index ...
We consider generalizations of this problem: mixtures of linear regressions, and mixtures of linear classifiers, where the goal is to recover supports of multiple sparse vectors using only a small number ...
This work is supported in part by NSF awards 2133484, 2127929, and 1934846. ...
arXiv:2106.05951v2
fatcat:zaz4es42frhixk3vfhatmfxfby
Bayesian framework and message passing for joint support and signal recovery of approximately sparse signals
2011
2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2012; you can also visit the original URL.
The file type is application/pdf.
The problem of recovery of strictly sparse signals from noisy measurements can be viewed as a problem of recovery of approximately sparse signals from noiseless measurements, making the approach applicable ...
to strictly sparse signal recovery from noisy measurements. ...
Fig. 4 . 4 % success versus L performance of support recovery of strictly sparse signals with noisy measurements for MMV. ...
doi:10.1109/icassp.2011.5947237
dblp:conf/icassp/ShedthikereC11
fatcat:pdy3vdakbnburfdbqtjrhzryi4
Information-Theoretic Limits on Sparse Signal Recovery: Dense versus Sparse Measurement Matrices
2010
IEEE Transactions on Information Theory
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2011; you can also visit the original URL.
The file type is application/pdf.
We study the information-theoretic limits of exactly recovering the support set of a sparse signal, using noisy projections defined by various classes of measurement matrices. ...
size n, including the important special case of linear sparsity (k = 2(p)) using a linear scaling of observations (n = 2(p)). ...
ACKNOWLEDGMENT The authors would like to thank the anonymous reviewers for helpful comments that improved the presentation of this paper. ...
doi:10.1109/tit.2010.2046199
fatcat:juxxztaykzai5p7lxjulktv7di
Model-based compressive sensing for multi-party distant speech recognition
2011
2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
We leverage the recent algorithmic advances in compressive sensing, and propose a novel source separation algorithm for efficient recovery of convolutive speech mixtures in spectro-temporal domain. ...
Our results provide compelling evidence of the effectiveness of sparse recovery formulations in speech recognition. ...
A new paradigm in CS exploits the inter-dependency structure underlying the support of the sparse coefficients in recovery algorithms to reduce the number of required measurements and to better differentiate ...
doi:10.1109/icassp.2011.5947379
dblp:conf/icassp/AsaeiBC11
fatcat:gzadkg4t6jdthkwirfqdm2texa
An Empirical-Bayes Approach to Recovering Linearly Constrained Non-Negative Sparse Signals
2014
IEEE Transactions on Signal Processing
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2016; you can also visit the original URL.
The file type is application/pdf.
Other Versions
We propose two novel approaches to the recovery of an (approximately) sparse signal from noisy linear measurements in the case that the signal is a priori known to be non-negative and obey given linear ...
Our second approach is based on the sum-product version of the GAMP algorithm, where we propose the use of a Bernoulli non-negative Gaussian-mixture signal prior and a Laplacian likelihood, and propose ...
INTRODUCTION We consider the recovery of an (approximately) sparse signal x ∈ R N from the noisy linear measurements y = Ax + w ∈ R M , (1) where A is a known sensing matrix, w is additive white Gaussian ...
doi:10.1109/tsp.2014.2337841
fatcat:yxrwi3hrfvfzdhfk4644zhu34m
Sparse source separation from orthogonal mixtures
2009
2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
This paper addresses source separation from a linear mixture under two assumptions: source sparsity and orthogonality of the mixing matrix. ...
In the second stage, the support is used to reformulate the recovery task as an optimization problem. We then suggest a solution based on alternating minimization. ...
INTRODUCTION Blind source separation (BSS) is a fundamental problem in data analysis where the goal is to recover a set of source signals from their linear mixture, typically in the presence of noise. ...
doi:10.1109/icassp.2009.4960291
dblp:conf/icassp/MishaliE09
fatcat:p3auzz6cmrcmvem3w7t5bhhjsi
An empirical-bayes approach to recovering linearly constrained non-negative sparse signals
2013
2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2016; you can also visit the original URL.
The file type is application/pdf.
We consider the recovery of an (approximately) sparse signal from noisy linear measurements, in the case that the signal is apriori known to be non-negative and obeys certain linear equality constraints ...
To enforce both sparsity and nonnegativity, we employ an i.i.d Bernoulli non-negative Gaussian mixture (NNGM) prior and perform approximate minimum mean-squared error (MMSE) recovery of the signal using ...
INTRODUCTION We consider the recovery of an (approximately) sparse signal x ∈ R N from the noisy linear measurements y = Ax + w ∈ R M , (1) where A is a known sensing matrix, w is additive white Gaussian ...
doi:10.1109/camsap.2013.6713993
dblp:conf/camsap/VilaS13
fatcat:td66wc7p35eynfxpc2kejrmu6i
Distributed compressed sensing of Hyperspectral images via blind source separation
2010
2010 Conference Record of the Forty Fourth Asilomar Conference on Signals, Systems and Computers
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2016; you can also visit the original URL.
The file type is application/pdf.
In this work, we assume few number of sources are generating the multichannel observations based on a linear mixture model. ...
algorithm to estimate both the sources and the mixing matrix (and thus the whole data) from the compressed measurements. ...
Unlike the other methods for multichannel CS, our scheme attempts to improve recovery by exploiting dependencies across the channels via assuming a linear source mixture model for the signals. ...
doi:10.1109/acssc.2010.5757497
fatcat:jpyr2ubisrhh3akl725ygtab7e
Sparse Signal Recovery from a Mixture of Linear and Magnitude-Only Measurements
2015
IEEE Signal Processing Letters
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
We consider the problem of exact sparse signal recovery from a combination of linear and magnitude-only (phaseless) measurements. ...
We show that if max(2m 1 , 1) + m 2 ≥ 4k − 1, then a set of generic measurements are sufficient to recover every k-sparse x exactly, establishing the trade-off between the number of linear and magnitude-only ...
Akçakaya would like to acknowledge grant support from NIH K99HL111410-01. ...
doi:10.1109/lsp.2015.2393295
pmid:29187781
pmcid:PMC5703438
fatcat:zatjlransfcuzezdb6s5qiap5e
On Recovery of Sparse Signals in Compressed DNA Microarrays
2007
Asilomar Conference on Signals, Systems and Computers. Conference Record
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
To recover signals from compressed microarray measurements, we leverage ideas from compressive sampling. Experimental verification of the proposed methodology is presented. ...
This is a wasteful use of the sensing resources in comparative DNA microarray experiments, where a test sample is measured relative to a reference sample. ...
In particular, recall that a sparse signal may be recovered from a small number of linear combinations of its components. ...
doi:10.1109/acssc.2007.4487303
fatcat:6h4wulhrnbcjplbvmzdjparpoe
Sparsity estimation from compressive projections via sparse random matrices
2018
EURASIP Journal on Advances in Signal Processing
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
The aim of this paper is to develop strategies to estimate the sparsity degree of a signal from compressive projections, without the burden of recovery. ...
The proposed method employs γ-sparsified random matrices and is based on a maximum likelihood (ML) approach, exploiting the property that the acquired measurements are distributed according to a mixture ...
Acknowledgments The authors thank the European Research Council for the financial support for this research. ...
doi:10.1186/s13634-018-0578-0
pmid:30956656
pmcid:PMC6414084
fatcat:oz45y6yikzhhxd6wdnyxdn24fu
A Distributed Compressive Sensing Technique for Data Gathering in Wireless Sensor Networks
2013
Procedia Computer Science
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf.
The strength of compressive sensing is its ability to reconstruct sparse or compressible signals from small number of measurements without requiring any a priori knowledge about the signal structure. ...
Our approach employs Bayesian inference to build probabilistic model of the signals and thereafter applies belief propagation algorithm as a decoding method to recover the common sparse signal. ...
Compressive sensing theory states that if signal is K-sparse on basis, it can be captured and recovered from M nonadaptive, linear measurements ( ) with a certain restriction. ...
doi:10.1016/j.procs.2013.09.028
fatcat:dzst6s6lbba45gmwplqlgpmdqe
Interval-Passing Algorithm for Chemical Mixture Estimation
2013
IEEE Signal Processing Letters
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2014; you can also visit the original URL.
The file type is application/pdf.
We show that by applying an appropriate measurement matrix on the chemical mixture spectrum, we obtain an overall measurement matrix which is sparse. ...
Simulation results for the proportion of correct reconstructions show that chemical mixtures with a large number of chemicals present can be recovered. ...
Instead of using a measurement matrix M which is sparse, the AMP requires M to be a random matrix whose elements are i.i.d. drawn from some distribution, taken here to be Gaussian. ...
doi:10.1109/lsp.2013.2267656
fatcat:46kamev6ffc7pcottktnkhglw4
Information-theoretic limits on sparse support recovery: Dense versus sparse measurements
2008
2008 IEEE International Symposium on Information Theory
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2008; you can also visit the original URL.
The file type is application/pdf.
We study the information-theoretic limits of exactly recovering the support of a sparse signal using noisy projections defined by various classes of measurement matrices. ...
We derive lower bounds on the number of observations required for exact sparsity recovery, as a function of the signal dimension p, signal sparsity k, and measurement sparsity γ. ...
Acknowledgment The work of WW and KR was supported by NSF grant CCF-0635114. The work of MJW was supported by NSF grants CAREER-CCF-0545862 and DMS-0605165. ...
doi:10.1109/isit.2008.4595380
dblp:conf/isit/WangWR08
fatcat:ujvawejhjbcctmqs6emlagwcwu
Information-theoretic limits on sparse signal recovery: Dense versus sparse measurement matrices
[article]
2008
arXiv
pre-print
Preserved Fulltext
The Internet Archive has a preservation copy of this work in our general collections.
The file type is application/pdf.
We study the information-theoretic limits of exactly recovering the support of a sparse signal using noisy projections defined by various classes of measurement matrices. ...
n, including the important special case of linear sparsity (k = Θ(p)) using a linear scaling of observations (n = Θ(p)). ...
Acknowledgment The work of WW and KR was supported by NSF grant CCF-0635114. The work of MJW was supported by NSF grants CAREER-CCF-0545862 and DMS-0605165. ...
arXiv:0806.0604v1
fatcat:kpb62qx6qrat5l4cjv2ki4uxvq
« Previous
Showing results 1 — 15 out of 13,211 results