L1-Minimization Algorithm for Bayesian Online Compressed Sensing

Rossi, Patrícia de; Vicente, Renato

doi:10.3390/e19120667

Cited by 5 publications

(6 citation statements)

References 34 publications

(52 reference statements)

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“…L1-minimization Algorithm for Bayesian Online Compressed Sensing [ 3 ] proposed a Bayesian online reconstruction algorithm for sparse signals based on Compressed Sensing and inspired by L1-regularization schemes. It showed that, even when prior knowledge about the signal’s generation is limited, reconstruction is possible by introduction of a Laplace prior and of an extra Kullback–Leibler divergence minimization step for hyper-parameter learning.…”

Section: Entropy Special Issue and Conference Proceedingsmentioning

confidence: 99%

Overview of the 37th MaxEnt

Dias

Diniz

Pereira

et al. 2018

Entropy

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The 37th edition of MaxEnt was held in Brazil, hosting several distinguished researchers and students. The workshop offered four tutorials, nine invited talks, twenty four oral presentations and twenty seven poster presentations. All submissions received their first choice between oral and poster presentations. The event held a celebration to Julio Stern’s 60th anniversary and awarded two prizes to young researchers. As customary, the workshop had one free afternoon, in which participants visited the city’s surroundings and experienced Brazilian food and traditions.

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Section: Entropy Special Issue and Conference Proceedingsmentioning

confidence: 99%

Overview of the 37th MaxEnt

Dias

Diniz

Pereira

et al. 2018

Entropy

View full text Add to dashboard Cite

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“…The second one is known as relaxation which replaces the zero-norm function with a smoother one and uses optimization technique to find the solution [10]. Algorithms such as orthogonal matching pursuit (OMP), smoothed l 0 (SL 0 ) and l 1 -minimization [11]- [13] are some examples of the proposed algorithms for finding the sparse solution.…”

Section: Introductionmentioning

confidence: 99%

A new framework for utilizing side information in sparse representation

Rafsanjani

Maghrebi

2021

IJEECS

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An underdetermined system of linear equation has infinitely number of answers. To find a specific solution, regularization method is used. For this propose, we define a cost function based on desired features of the solution and that answer with the best matches to these function is selected as the desired solution. In case of sparse solution, zero-norm function is selected as the cost function. In many engineering cases, there is side information which are omitted because of the zero-norm function. Finding a way to conquer zero-norm function limitation, will help to improve estimation of the desired parameter. In this regard, we utilize maximum a posterior (MAP) estimation and modify the prior information such that both sparsity and side information are utilized. As a consequence, a framework to utilize side information into sparse representation algorithms is proposed. We also test our proposed framework in orthogonal frequency division multiplexing (OFDM) sparse channel estimation problem which indicates, by utilizing our proposed system, the performance of the system improves and fewer resources are required for estimating the channel.

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“…They achieve better performance than the method in [10] with the same number of nonzero taps. Furthermore, for the pursuit of both sparse promotion and improved accuracy, the sparse signals reconstruction algorithms inspired by l 1 and weighted l 1 regularization schemes are proposed in [17,18]; the joint smoothed l 0 norm algorithm for direction-of-arrival estimation in MIMO radar is proposed in [19]. …”

Section: Introductionmentioning

confidence: 99%

Design of Sparse FIR Decision Feedback Equalizers in MIMO Systems Using Hybrid l1/l2 Norm Minimization and the OMP Algorithm

Zhao

et al. 2018

Sensors

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In this paper, a novel scheme using hybrid l1/l2 norm minimization and the orthogonal matching pursuit (OMP) algorithm is proposed to design the sparse finite impulse response (FIR) decision feedback equalizers (DFE) in multiple input multiple output (MIMO) systems. To reduce the number of nonzero taps for the FIR DFE while ensuring its design accuracy, the problem of designing a sparse FIR DFE is transformed into an l0 norm minimization problem, and then the proposed scheme is used to obtain the sparse solution. In the proposed scheme, a sequence of minimum weighted l2 norm problems is solved using the OMP algorithm. The nonzero taps positions can be corrected with the different weights in the diagonal weighting matrix which is computed through the hybrid l1/l2 norm minimization. The simulation results verify that the sparse FIR MIMO DFEs designed by the proposed scheme get a significant reduction in the number of nonzero taps with a small performance loss compared to the non-sparse optimum DFE under the minimum mean square error (MMSE) criterion. In addition, the proposed scheme provides better design accuracy than the OMP algorithm with the same sparsity level.

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L1-Minimization Algorithm for Bayesian Online Compressed Sensing

Cited by 5 publications

References 34 publications

Overview of the 37th MaxEnt

Overview of the 37th MaxEnt

A new framework for utilizing side information in sparse representation

Design of Sparse FIR Decision Feedback Equalizers in MIMO Systems Using Hybrid l1/l2 Norm Minimization and the OMP Algorithm

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