Weighted thresholding homotopy method for sparsity constrained optimization
Sign Up to like & getrecommendations! Published in 2020 at "Journal of Combinatorial Optimization"
DOI: 10.1007/s10878-020-00563-7
Abstract: We propose in this paper a novel weighted thresholding method for the sparsity-constrained optimization problem. By reformulating the problem equivalently as a mixed-integer programming, we investigate the Lagrange duality with respect to an $$l_1$$ l… read more here.
Keywords: method; weighted thresholding; optimization; constrained optimization ... See more keywords
Sparsity Constrained Fusion of Hyperspectral and Multispectral Images
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Geoscience and Remote Sensing Letters"
DOI: 10.1109/lgrs.2022.3146248
Abstract: Fusing a Hyperspectral image (HSI) and a multispectral image (MSI) from different sensors is an economic and effective approach to get an image with both high spatial and spectral resolution, but localized changes between the… read more here.
Keywords: constrained fusion; sparsity constrained; hyperspectral msis; fusion hyperspectral ... See more keywords
L₁ Sparsity-Constrained Archetypal Analysis Algorithm for Hyperspectral Unmixing
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Geoscience and Remote Sensing Letters"
DOI: 10.1109/lgrs.2022.3164054
Abstract: Hyperspectral unmixing (HU) is widely used to process mixed pixels as an essential technology. Among them, the nonnegative matrix factorization (NMF)-based approach is one typical of the blind unmixing techniques, which can achieve endmembers and… read more here.
Keywords: sparsity constrained; hyperspectral unmixing; archetypal analysis; method ... See more keywords
Deep Canonical Correlation Analysis Using Sparsity-Constrained Optimization for Nonlinear Process Monitoring
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Industrial Informatics"
DOI: 10.1109/tii.2021.3121770
Abstract: This article proposes an efficient nonlinear process monitoring method (DCCA-SCO) by integrating canonical correlation analysis (CCA), deep autoencoder neural networks (DAENNs), and sparsity-constrained optimization (SCO). Specifically, DAENNs are first used to learn a nonlinear function… read more here.
Keywords: process monitoring; correlation analysis; sparsity constrained; process ... See more keywords
On the Foundation of Sparsity Constrained Sensing— Part I: Sampling Theory and Robust Remainder Problem
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Signal Processing"
DOI: 10.1109/tsp.2023.3247138
Abstract: In the first part of the series papers, we set out to answer the following fundamental question: for constrained sampling, what kind of signal can be uniquely represented or recovered by the (distributed) discrete sample… read more here.
Keywords: sensing italic; sparsity; robust remainder; sparsity constrained ... See more keywords
A Gradient Projection Algorithm with a New Stepsize for Nonnegative Sparsity-Constrained Optimization
Sign Up to like & getrecommendations! Published in 2020 at "Mathematical Problems in Engineering"
DOI: 10.1155/2020/6489190
Abstract: Nonnegative sparsity-constrained optimization problem arises in many fields, such as the linear compressing sensing problem and the regularized logistic regression cost function. In this paper, we introduce a new stepsize rule and establish a gradient… read more here.
Keywords: projection algorithm; nonnegative sparsity; new stepsize; gradient projection ... See more keywords