Proximal algorithms

N Parikh, S Boyd - Foundations and trends® in Optimization, 2014 - nowpublishers.com
This monograph is about a class of optimization algorithms called proximal algorithms. Much
like Newton's method is a standard tool for solving unconstrained smooth optimization …

Adaptive spatiotemporal SVD clutter filtering for ultrafast Doppler imaging using similarity of spatial singular vectors

J Baranger, B Arnal, F Perren, O Baud… - IEEE transactions on …, 2018 - ieeexplore.ieee.org
Singular value decomposition of ultrafast imaging ultrasonic data sets has recently been
shown to build a vector basis far more adapted to the discrimination of tissue and blood flow …

From denoising to compressed sensing

CA Metzler, A Maleki… - IEEE Transactions on …, 2016 - ieeexplore.ieee.org
A denoising algorithm seeks to remove noise, errors, or perturbations from a signal.
Extensive research has been devoted to this arena over the last several decades, and as a …

Low‐rank plus sparse matrix decomposition for accelerated dynamic MRI with separation of background and dynamic components

R Otazo, E Candes… - Magnetic resonance in …, 2015 - Wiley Online Library
Purpose To apply the low‐rank plus sparse (L+ S) matrix decomposition model to
reconstruct undersampled dynamic MRI as a superposition of background and dynamic …

The Optimal Hard Threshold for Singular Values is

M Gavish, DL Donoho - IEEE Transactions on Information …, 2014 - ieeexplore.ieee.org
We consider recovery of low-rank matrices from noisy data by hard thresholding of singular
values, in which empirical singular values below a threshold are set to 0. We study the …

Different approaches for human activity recognition: A survey

Z Hussain, M Sheng, WE Zhang - arxiv preprint arxiv:1906.05074, 2019 - arxiv.org
Human activity recognition has gained importance in recent years due to its applications in
various fields such as health, security and surveillance, entertainment, and intelligent …

Tensor SVD: Statistical and computational limits

A Zhang, D **a - IEEE Transactions on Information Theory, 2018 - ieeexplore.ieee.org
In this paper, we propose a general framework for tensor singular value decomposition
(tensor singular value decomposition (SVD)), which focuses on the methodology and theory …