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Sparse signal recovery using iterative proximal projection
, Article IEEE Transactions on Signal Processing ; Volume 66, Issue 4 , 2018 , Pages 879-894 ; 1053587X (ISSN) ; Sadeghi, M ; Babaie Zadeh, M ; Chatterjee, S ; Skoglund, M ; Jutten, C ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2018
Abstract
This paper is concerned with designing efficient algorithms for recovering sparse signals from noisy underdetermined measurements. More precisely, we consider minimization of a nonsmooth and nonconvex sparsity promoting function subject to an error constraint. To solve this problem, we use an alternating minimization penalty method, which ends up with an iterative proximal-projection approach. Furthermore, inspired by accelerated gradient schemes for solving convex problems, we equip the obtained algorithm with a so-called extrapolation step to boost its performance. Additionally, we prove its convergence to a critical point. Our extensive simulations on synthetic as well as real data verify...
Spectral Sparsification of Graph
, M.Sc. Thesis Sharif University of Technology ; Daneshgar, Amir (Supervisor)
Abstract
The running time of almost every algorithm in the graph theory depend on the number of edges. Thus, these algorithms are often too slow when the input graphs are dense. Therefore, it is useful to reduce the number of edges by sparsification. In fact, sparsification is the task of approximating a graph G = (V;E) by another graph ~G = (V; ~E) so that ~E E (j~Ej jEj) and ~G maintain a main prefixed property of G. Depending on these properties several notions of graph sparsification have been proposed. In this thesis we study a notion of sparsification that is called spectral sparsification which is based on the contributions of Daniel A. Spielman et.al..In this notion of sparsification...
Multiclass classification of patients during different stages of Alzheimer's disease using fMRI time-series
, Article Biomedical Physics and Engineering Express ; Volume 6, Issue 5 , 2020 ; Fatemizadeh, E ; Motie Nasrabadi, A ; Sharif University of Technology
IOP Publishing Ltd
2020
Abstract
Alzheimer's Disease (AD) begins several years before the symptoms develop. It starts with Mild Cognitive Impairment (MCI) which can be separated into Early MCI and Late MCI (EMCI and LMCI). Functional connectivity analysis and classification are done among the different stages of illness with Functional Magnetic Resonance Imaging (fMRI). In this study, in addition to the four stages including healthy, EMCI, LMCI, and AD, the patients have been tracked for a year. Indeed, the classification has been done among 7 groups to analyze the functional connectivity changes in one year in different stages. After generating the functional connectivity graphs for eliminating the weak links, three...