Nonlinear Regularization of Inverse Problems for Linear Homogeneous Transforms by Stabilized Hard Thresholding
Sign Up to like & getrecommendations! Published in 2018 at "Journal of Mathematical Sciences"
DOI: 10.1007/s10958-018-4044-1
Abstract: In this paper we consider the problem of inverting linear homogeneous transforms by Vaguelette–Wavelet decomposition and stabilized hard thresholding of noisy wavelet coefficients. We also prove asymptotic normality and strong consistency of the mean-square risk… read more here.
Keywords: nonlinear regularization; stabilized hard; homogeneous transforms; hard thresholding ... See more keywords
Iterative hard thresholding for low-rank recovery from rank-one projections
Sign Up to like & getrecommendations! Published in 2019 at "Linear Algebra and its Applications"
DOI: 10.1016/j.laa.2019.03.007
Abstract: A novel algorithm for the recovery of low-rank matrices acquired via compressive linear measurements is proposed and analyzed. The algorithm, a variation on the iterative hard thresholding algorithm for low-rank recovery, is designed to succeed… read more here.
Keywords: iterative hard; low rank; rank; rank recovery ... See more keywords
Anti-Fragmentation of Resting-State Functional Magnetic Resonance Imaging Connectivity Networks with Node-Wise Thresholding
Sign Up to like & getrecommendations! Published in 2017 at "Brain connectivity"
DOI: 10.1089/brain.2017.0523
Abstract: Functional magnetic resonance imaging (fMRI)-based functional connectivity networks are often constructed by thresholding a correlation matrix of nodal time courses. In a typical thresholding approach known as hard thresholding, a single threshold is applied to… read more here.
Keywords: network; connectivity; node wise; hard thresholding ... See more keywords
An Armijo-Type Hard Thresholding Algorithm for Joint Sparse Recovery
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DOI: 10.1109/access.2021.3097216
Abstract: Joint sparse recovery (JSR) in compressed sensing simultaneously recovers sparse signals with a common sparsity structure from their multiple measurement vectors obtained through a common sensing matrix. In this paper, we present an Armijo-type hard… read more here.
Keywords: armijo type; joint sparse; type hard; hard thresholding ... See more keywords
Iterative Hard Thresholding Algorithm-Based Detector for Compressed OFDM-IM Systems
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Communications Letters"
DOI: 10.1109/lcomm.2022.3187451
Abstract: A low-complexity iterative hard thresholding (IHT) algorithm-based detector is proposed for the compressed orthogonal frequency division multiplexing with index modulation (OFDM-IM) system. To improve recovery accuracy and reduce the number of iterations, detection is embedded… read more here.
Keywords: iterative hard; algorithm based; thresholding algorithm; based detector ... See more keywords
Lorentzian Based Adaptive Filters for Impulsive Noise Environments
Sign Up to like & getrecommendations! Published in 2017 at "IEEE Transactions on Circuits and Systems I: Regular Papers"
DOI: 10.1109/tcsi.2017.2667705
Abstract: In this paper, three Lorentzian based robust adaptive algorithms are proposed for identifying systems in presence of impulsive noise. The first algorithm called Lorentzian adaptive filtering (LAF) is derived from a sliding window type cost… read more here.
Keywords: lorentzian based; hard thresholding; algorithm called; impulsive noise ... See more keywords
Partial Hard Thresholding
Sign Up to like & getrecommendations! Published in 2017 at "IEEE Transactions on Information Theory"
DOI: 10.1109/tit.2017.2686880
Abstract: We study iterative algorithms for compressed sensing that have an “orthogonalization” step at each iteration to keep the residual orthogonal to the span of those columns of the measurement matrix that have been selected so… read more here.
Keywords: iterative algorithms; partial hard; hard thresholding; pht operator ... See more keywords
Efficient Gradient Support Pursuit With Less Hard Thresholding for Cardinality-Constrained Learning.
Sign Up to like & getrecommendations! Published in 2021 at "IEEE transactions on neural networks and learning systems"
DOI: 10.1109/tnnls.2021.3087805
Abstract: Recently, stochastic hard thresholding (HT) optimization methods [e.g., stochastic variance reduced gradient hard thresholding (SVRGHT)] are becoming more attractive for solving large-scale sparsity/rank-constrained problems. However, they have much higher HT oracle complexities, especially for high-dimensional… read more here.
Keywords: gradient support; hard thresholding; support pursuit; stochastic variance ... See more keywords
Deterministic and Randomized Diffusion Based Iterative Generalized Hard Thresholding (DiFIGHT) for Distributed Recovery of Sparse Signals
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Signal and Information Processing over Networks"
DOI: 10.1109/tsipn.2021.3124362
Abstract: In this paper, we propose a distributed iterative hard thresholding algorithm, namely, DiFIGHT, for a network that uses diffusion as the means of intra-network collaboration. Subsequently, we present a modification of the proposed algorithm, namely,… read more here.
Keywords: diffusion; tex math; inline formula; hard thresholding ... See more keywords
Iterative Difference Hard-Thresholding Algorithm for Sparse Signal Recovery
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Signal Processing"
DOI: 10.1109/tsp.2023.3262184
Abstract: In this paper, a nonconvex surrogate function, namely, Laplace norm, is studied to recover the sparse signals. Firstly, we discuss the equivalence of the optimal solutions of $l_{0}$-norm minimization problem, Laplace norm minimization problem and… read more here.
Keywords: thresholding algorithm; hard thresholding; difference hard; minimization problem ... See more keywords
Federated Optimization of ℓ0-norm Regularized Sparse Learning
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DOI: 10.3390/a15090319
Abstract: Regularized sparse learning with the ℓ0-norm is important in many areas, including statistical learning and signal processing. Iterative hard thresholding (IHT) methods are the state-of-the-art for nonconvex-constrained sparse learning due to their capability of recovering… read more here.
Keywords: regularized sparse; federated optimization; hard thresholding; sparse learning ... See more keywords