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 …
Low rank tensor completion for multiway visual data
Tensor completion recovers missing entries of multiway data. The missing of entries could
often be caused during the data acquisition and transformation. In this paper, we provide an …
often be caused during the data acquisition and transformation. In this paper, we provide an …
Semantic image inpainting with deep generative models
Semantic image inpainting is a challenging task where large missing regions have to be
filled based on the available visual data. Existing methods which extract information from …
filled based on the available visual data. Existing methods which extract information from …
Weighted nuclear norm minimization and its applications to low level vision
As a convex relaxation of the rank minimization model, the nuclear norm minimization
(NNM) problem has been attracting significant research interest in recent years. The …
(NNM) problem has been attracting significant research interest in recent years. The …
Tensor robust principal component analysis: Exact recovery of corrupted low-rank tensors via convex optimization
This paper studies the Tensor Robust Principal Component (TRPCA) problem which
extends the known Robust PCA to the tensor case. Our model is based on a new tensor …
extends the known Robust PCA to the tensor case. Our model is based on a new tensor …
Weighted Schatten -Norm Minimization for Image Denoising and Background Subtraction
Low rank matrix approximation (LRMA), which aims to recover the underlying low rank
matrix from its degraded observation, has a wide range of applications in computer vision …
matrix from its degraded observation, has a wide range of applications in computer vision …
HRST-LR: a hessian regularization spatio-temporal low rank algorithm for traffic data imputation
Intelligent Transportation Systems (ITSs) are vital for alleviating traffic congestion and
improving traffic efficiency. Due to the delay of network transmission and failure of detectors …
improving traffic efficiency. Due to the delay of network transmission and failure of detectors …
Nonconvex nonsmooth low rank minimization via iteratively reweighted nuclear norm
The nuclear norm is widely used as a convex surrogate of the rank function in compressive
sensing for low rank matrix recovery with its applications in image recovery and signal …
sensing for low rank matrix recovery with its applications in image recovery and signal …
Learning deep sparse regularizers with applications to multi-view clustering and semi-supervised classification
Sparsity-constrained optimization problems are common in machine learning, such as
sparse coding, low-rank minimization and compressive sensing. However, most of previous …
sparse coding, low-rank minimization and compressive sensing. However, most of previous …
A nonconvex low-rank tensor completion model for spatiotemporal traffic data imputation
Sparsity and missing data problems are very common in spatiotemporal traffic data collected
from various sensing systems. Making accurate imputation is critical to many applications in …
from various sensing systems. Making accurate imputation is critical to many applications in …