A comprehensive survey on regularization strategies in machine learning
Y Tian, Y Zhang - Information Fusion, 2022 - Elsevier
In machine learning, the model is not as complicated as possible. Good generalization
ability means that the model not only performs well on the training data set, but also can …
ability means that the model not only performs well on the training data set, but also can …
A comprehensive survey of sparse regularization: Fundamental, state-of-the-art methodologies and applications on fault diagnosis
Q Li - Expert Systems with Applications, 2023 - Elsevier
Sparse regularization has been attracting much attention in industrial applications over the
past few decades. By exploiting the latent data structure in low-dimensional subspaces, a …
past few decades. By exploiting the latent data structure in low-dimensional subspaces, a …
Sparse regularization via convex analysis
I Selesnick - IEEE Transactions on Signal Processing, 2017 - ieeexplore.ieee.org
Sparse approximate solutions to linear equations are classically obtained via L1 norm
regularized least squares, but this method often underestimates the true solution. As an …
regularized least squares, but this method often underestimates the true solution. As an …
A survey on some recent developments of alternating direction method of multipliers
DR Han - Journal of the Operations Research Society of China, 2022 - Springer
Recently, alternating direction method of multipliers (ADMM) attracts much attentions from
various fields and there are many variant versions tailored for different models. Moreover, its …
various fields and there are many variant versions tailored for different models. Moreover, its …
A sparse domain adaption network for remaining useful life prediction of rolling bearings under different working conditions
M Miao, J Yu, Z Zhao - Reliability Engineering & System Safety, 2022 - Elsevier
As a key component in the machinery, the health of bearings directly affects working
performance of machinery. Recently, many data-driven methods have been proposed to …
performance of machinery. Recently, many data-driven methods have been proposed to …
Group-sparse signal denoising: non-convex regularization, convex optimization
Convex optimization with sparsity-promoting convex regularization is a standard approach
for estimating sparse signals in noise. In order to promote sparsity more strongly than …
for estimating sparse signals in noise. In order to promote sparsity more strongly than …
Improved shift-invariant sparse parsing of mechanical fault based on feature atom
C Han, W Lu, L Cui, L Song… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
In complex operating conditions, the monitoring signals of mechanical equipment are
susceptible to interference from multiple vibration sources and environmental noise …
susceptible to interference from multiple vibration sources and environmental noise …
An enhanced sparse representation-based intelligent recognition method for planet bearing fault diagnosis in wind turbines
Fault diagnosis techniques are vital to the condition-based maintenance strategy of wind
turbines, which enables the reliable and economical operation and maintenance for wind …
turbines, which enables the reliable and economical operation and maintenance for wind …
Artifact-free wavelet denoising: Non-convex sparse regularization, convex optimization
Algorithms for signal denoising that combine wavelet-domain sparsity and total variation
(TV) regularization are relatively free of artifacts, such as pseudo-Gibbs oscillations …
(TV) regularization are relatively free of artifacts, such as pseudo-Gibbs oscillations …
Total variation denoising via the Moreau envelope
I Selesnick - IEEE Signal Processing Letters, 2017 - ieeexplore.ieee.org
Total variation denoising is a nonlinear filtering method well suited for the estimation of
piecewise-constant signals observed in additive white Gaussian noise. The method is …
piecewise-constant signals observed in additive white Gaussian noise. The method is …