A Study on GMLVQ Convex and Non-convex Regularization

2016 ◽  
pp. 305-314 ◽  
Author(s):  
David Nova ◽  
Pablo A. Estévez
Keyword(s):  
Convex Regularization

scholarly journals Weak Fault Detection of Tapered Rolling Bearing Based on Penalty Regularization Approach

Algorithms ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 184 ◽  
Author(s):  
Qing Li ◽  
Steven Liang
Keyword(s):  
Vibration Signal ◽  
Singular Values ◽  
Rolling Bearing ◽  
L1 Norm ◽  
Detection Approach ◽  
The Matrix ◽  
Noisy Observation ◽  
Weak Fault ◽  
Convex Regularization ◽  
Fault Information

Aimed at the issue of estimating the fault component from a noisy observation, a novel detection approach based on augmented Huber non-convex penalty regularization (AHNPR) is proposed. The core objectives of the proposed method are that (1) it estimates non-zero singular values (i.e., fault component) accurately and (2) it maintains the convexity of the proposed objective cost function (OCF) by restricting the parameters of the non-convex regularization. Specifically, the AHNPR model is expressed as the L1-norm minus a generalized Huber function, which avoids the underestimation weakness of the L1-norm regularization. Furthermore, the convexity of the proposed OCF is proved via the non-diagonal characteristic of the matrix BTB, meanwhile, the non-zero singular values of the OCF is solved by the forward–backward splitting (FBS) algorithm. Last, the proposed method is validated by the simulated signal and vibration signals of tapered bearing. The results demonstrate that the proposed approach can identify weak fault information from the raw vibration signal under severe background noise, that the non-convex penalty regularization can induce sparsity of the singular values more effectively than the typical convex penalty (e.g., L1-norm fused lasso optimization (LFLO) method), and that the issue of underestimating sparse coefficients can be improved.

scholarly journals A Fast and Accurate Sparse Continuous Signal Reconstruction by Homotopy DCD with Non-Convex Regularization

Sensors ◽  
2014 ◽  
Vol 14 (4) ◽  
pp. 5929-5951 ◽  
Author(s):  
Tianyun Wang ◽  
Xinfei Lu ◽  
Xiaofei Yu ◽  
Zhendong Xi ◽  
Weidong Chen
Keyword(s):  
Signal Reconstruction ◽  
Continuous Signal ◽  
Convex Regularization

scholarly journals Group-based sparse representation for image compressive sensing reconstruction with non-convex regularization

2018 ◽  
Vol 296 ◽  
pp. 55-63 ◽  
Author(s):  
Zhiyuan Zha ◽  
Xinggan Zhang ◽  
Qiong Wang ◽  
Lan Tang ◽  
Xin Liu
Keyword(s):  
Compressive Sensing ◽  
Sparse Representation ◽  
Convex Regularization

A non-convex regularization based line artefact quantification method in lung ultrasound imagery for pulmonary disease evaluation

2020 ◽  
Vol 148 (4) ◽  
pp. 2735-2735
Author(s):  
Oktay Karakus ◽  
Nantheera Anantrasirichai ◽  
Adrian Basarab ◽  
Alin Achim
Keyword(s):  
Pulmonary Disease ◽  
Lung Ultrasound ◽  
Quantification Method ◽  
Convex Regularization

scholarly journals Convex Image Denoising via Non-convex Regularization with Parameter Selection

2016 ◽  
Vol 56 (2) ◽  
pp. 195-220 ◽  
Author(s):  
Alessandro Lanza ◽  
Serena Morigi ◽  
Fiorella Sgallari
Keyword(s):  
Image Denoising ◽  
Parameter Selection ◽  
Convex Regularization

scholarly journals Convex 1-D Total Variation Denoising with Non-convex Regularization

2015 ◽  
Vol 22 (2) ◽  
pp. 141-144 ◽  
Author(s):  
Ivan W. Selesnick ◽  
Ankit Parekh ◽  
Ilker Bayram
Keyword(s):  
Total Variation ◽  
Convex Regularization ◽  
Total Variation Denoising
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