19 citations · 162 across the 36 of their papers we have counts for
6 papers · 1 filter
Optimal Weighted Low-rank Matrix Recovery with Subspace Prior Information
Sajad Daei, Arash Amini, Farzan Haddadi
Matrix sensing is the problem of reconstructing a low-rank matrix from a few linear measurements. In many applications such as collaborative filtering, the famous Netflix prize pro…
Distribution-aware Block-sparse Recovery via Convex Optimization
Sajad Daei, Farzan Haddadi, Arash Amini
We study the problem of reconstructing a block-sparse signal from compressively sampled measurements. In certain applications, in addition to the inherent block-sparse structure of…
Improved Recovery of Analysis Sparse Vectors in Presence of Prior Information
Sajad Daei, Farzan Haddadi, Arash Amini
In this work, we consider the problem of recovering analysis-sparse signals from under-sampled measurements when some prior information about the support is available. We incorpora…
On the Error in Phase Transition Computations for Compressed Sensing
Sajad Daei, Farzan Haddadi, Arash Amini +1
Evaluating the statistical dimension is a common tool to determine the asymptotic phase transition in compressed sensing problems with Gaussian ensemble. Unfortunately, the exact e…
Exploiting Prior Information in Block Sparse Signals
Sajad Daei, Farzan Haddadi, Arash Amini
We study the problem of recovering a block-sparse signal from under-sampled observations. The non-zero values of such signals appear in few blocks, and their recovery is often acco…
Sample Complexity of Total Variation Minimization
Sajad Daei, Farzan Haddadi, Arash Amini
This work considers the use of Total variation (TV) minimization in the recovery of a given gradient sparse vector from Gaussian linear measurements. It has been shown in recent st…