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Matrix Completion via Sparse Factorization Solved by Accelerated Proximal Alternating Linearized Minimization

IEEE Transactions on Big Data, 2020
Classical matrix completion methods are not effective in recovering missing entries of data drawn from multiple subspaces because the matrices are often of high-rank. Recently a few advanced matrix completion methods were proposed to solve the problem but they are not scalable to large matrices and big data problems.
Tommy W S Chow, Jicong Fan, Mingbo Zhao
exaly   +3 more sources

A Gauss–Seidel type inertial proximal alternating linearized minimization for a class of nonconvex optimization problems

Journal of Global Optimization, 2019
The authors formulate an inertial version of the PALM method for minimizing the sum of two nonsmooth (possibly also nonconvex) functions in separate variables connected by a smooth coupling function. The inertial step is performed once of the two subproblems is addressed, it is formulated in the spirit of a Gauss-Seidel type method.
Deren Han, Xingju Cai
exaly   +3 more sources

Non-convex clustering via proximal alternating linearized minimization method

International Journal of Wavelets, Multiresolution and Information Processing, 2018
Clustering is a fundamental learning task in a wide range of research fields. The most popular clustering algorithm is arguably the K-means algorithm, it is well known that the performance of K-means algorithm heavily depends on initialization due to its strong non-convexity nature.
Ting Xie, Feiyu Chen
openaire   +2 more sources

Semi-blind image deblurring by a proximal alternating minimization method with convergence guarantees

Applied Mathematics and Computation, 2020
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Hong-Xia Dou   +4 more
openaire   +3 more sources

Efficient MR inhomogeneity correction by regularized entropy minimization and proximal alternations

2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), 2015
Magnetic Resonance (MR) images usually exhibit intensity inhomogeneity (bias field) due to non-uniformity generated from RF coils or radiation-patient interactions. This inhomogeneity could lead to undesired perception and difficult diagnosis. In this work, we propose a nonparametric retrospective bias corrector by minimizing a regularized-entropy ...
Bo Zhang, Hans Peeters
openaire   +1 more source

Linearized proximal alternating minimization algorithm for motion deblurring by nonlocal regularization

Pattern Recognition, 2011
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Sangwoon Yun, Hyenkyun Woo
openaire   +2 more sources

Proximal alternating minimization method for adaptive TGV-based image restoration

Multimedia Tools and Applications, 2020
This article presents an adaptive total generalized variation regularized strategy for image reconstruction. Unlike the traditional fixed weights schemes, our weights can be adaptively updated according to the latest computations. This helps to obtain more accurate numerical solutions and avoid the troublesome parameters selection.
openaire   +2 more sources

A proximal alternating linearization method for minimizing the sum of two convex functions

Science China Mathematics, 2015
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Zhang, WenXing, Cai, XingJu, Jia, ZeHui
openaire   +1 more source

Stochastic Gauss–Seidel type inertial proximal alternating linearized minimization and its application to proximal neural networks

Mathematical Methods of Operations Research
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Qingsong Wang, Deren Han
openaire   +2 more sources

A stochastic proximal alternating minimization algorithm for nonnegative matrix decomposition

Second International Conference on Applied Statistics, Computational Mathematics, and Software Engineering (ASCMSE 2023), 2023
Lijun Xu, Chenghua Ji, Yijia Zhou
openaire   +1 more source

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