most citedHigh-dimensional analysis of semidefinite relaxations for sparse principal components

123 citations · 148 across the 5 of their papers we have counts for

collaborators

5 papers

cs.IT20095 cited

Deterministic Construction of Compressed Sensing Matrices using BCH Codes

Arash Amini, Farokh Marvasti

In this paper we introduce deterministic RIP fulfilling matrices of order such that . Th…

cs.IT200913 cited

A Unified Approach to Sparse Signal Processing

F. Marvasti, A. Amini, F. Haddadi +6

A unified view of sparse signal processing is presented in tutorial form by bringing together various fields. For each of these fields, various algorithms and techniques, which hav…

cs.IT20097 cited

OFDM Channel Estimation Based on Adaptive Thresholding for Sparse Signal Detection

Mahdi Soltanolkotabi, Arash Amini, Farokh Marvasti

Wireless OFDM channels can be approximated by a time varying filter with sparse time domain taps. Recent achievements in sparse signal processing such as compressed sensing have fa…

cs.IT2009

Limits of Deterministic Compressed Sensing Considering Arbitrary Orthonormal Basis for Sparsity

Arash Amini, Farokh Marvasti

It is previously shown that proper random linear samples of a finite discrete signal (vector) which has a sparse representation in an orthonormal basis make it possible (with proba…

math.ST2008123 cited

High-dimensional analysis of semidefinite relaxations for sparse principal components

Arash A. Amini, Martin J. Wainwright

Principal component analysis (PCA) is a classical method for dimensionality reduction based on extracting the dominant eigenvectors of the sample covariance matrix. However, PCA is…