Faculty Bibliography
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This paper proposes vector minimax concave (VMC) penalty for sparse representation using tools of Moreau envelope. The VMC penalty is a weighted MC function; by fine tuning the weight of the VMC penalty with given strategy, the VMC regularized least squares problem shares the same global minimizers with the L-o regularization problem but has fewer local minima. Facilitated by the alternating direction method of multipliers (ADMM), the VMC regularization problem can be tackled as a sequence of convex sub-problems, each of which can be solved fast. Theoretical analysis of ADMM shows that the convergence of solving the VMC regularization problem is guaranteed. We present a series of numerical experiments demonstrating the superior performance of the VMC penalty and the ADMM algorithm in broad applications for sparse representation, including sparse denoising, sparse deconvolution, and missing data estimation. (C) 2018 Elsevier Inc. All rights reserved.
Vibration monitoring is one of the most effective ways for bearing fault diagnosis, and a challenge is how to accurately estimate bearing fault signals from noisy vibration signals. In this paper, a nonconvex sparse regularization method for bearing fault diagnosis is proposed based on the generalized minimax-concave (GMC) penalty, which maintains the convexity of the sparsity-regularized least squares cost function, and thus the global minimum can be solved by convex optimization algorithms. Furthermore, we introduce a k-sparsity strategy for the adaptive selection of the regularization parameter. The main advantage over conventional filtering methods is that GMC can better preserve the bearing fault signal while reducing the interference of noise and other components; thus, it can significantly improve the estimation accuracy of the bearing fault signal. A simulation study and two run-to-failure experiments verify the effectiveness of GMC in the diagnosis of localized faults in rolling bearings, and the comparison studies show that GMC provides more accurate estimation results than L1-norm regularization and spectral kurtosis.
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Total Results:
254
A convex-nonconvex variational method for the additive decomposition of functions on surfaces
ISI:000499907900001
ISSN: 0266-5611
CID: 4532902
NONLINEAR SMOOTHING OF DATA WITH RANDOM GAPS AND OUTLIERS (DRAGO) IMPROVES ESTIMATION OF CIRCADIAN RHYTHM [Meeting Abstract]
ISI:000471071001105
ISSN: 1550-9109
CID: 4532862
Sparsity-Inducing Nonconvex Nonseparable Regularization for Convex Image Processing
ISI:000473117100015
ISSN: 1936-4954
CID: 4532882
Wind Turbine Clutter Mitigation via Nonconvex Regularizers and Multidimensional Processing
ISI:000471658100001
ISSN: 0739-0572
CID: 4532872
Stable Principal Component Pursuit via Convex Analysis
ISI:000464941100007
ISSN: 1053-587x
CID: 4532842
Vector minimax concave penalty for sparse representation
ISI:000453637100016
ISSN: 1051-2004
CID: 3562262
Nonconvex Sparse Regularization and Convex Optimization for Bearing Fault Diagnosis
ISI:000431397500049
ISSN: 0278-0046
CID: 3127962
A Novel OCT Denoising Algorithm Based on Signal Decomposition and Constrained Wavelet Thresholding [Meeting Abstract]
ISI:000442912504296
ISSN: 0146-0404
CID: 3333522
Relation of Quantitative Eye Movements with Cognitive Dysfunction in Patients with Concussion [Meeting Abstract]
ISI:000453090805233
ISSN: 0028-3878
CID: 3561672
'Sandbagging' a Vision Test for Concussion-based Sideline Assessment: An Eye Movement Investigation Objectively Reveals the 'Gamers' Strategies [Meeting Abstract]
ISI:000453090801144
ISSN: 0028-3878
CID: 3561472
