Matrix analysis
Sparse Approximate Solutions to Linear Systems
SIAM Journal on Computing
Clustering Large Graphs via the Singular Value Decomposition
Machine Learning
Matrix approximation and projective clustering via volume sampling
SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
Fast Monte Carlo Algorithms for Matrices II: Computing a Low-Rank Approximation to a Matrix
SIAM Journal on Computing
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Exact Matrix Completion via Convex Optimization
Foundations of Computational Mathematics
Subspace pursuit for compressive sensing signal reconstruction
IEEE Transactions on Information Theory
Interior-Point Method for Nuclear Norm Approximation with Application to System Identification
SIAM Journal on Matrix Analysis and Applications
Matrix completion from a few entries
IEEE Transactions on Information Theory
ADMiRA: atomic decomposition for minimum rank approximation
IEEE Transactions on Information Theory
A Singular Value Thresholding Algorithm for Matrix Completion
SIAM Journal on Optimization
Limitations of matrix completion via trace norm minimization
ACM SIGKDD Explorations Newsletter
Convergence of Fixed-Point Continuation Algorithms for Matrix Rank Minimization
Foundations of Computational Mathematics
Robust principal component analysis?
Journal of the ACM (JACM)
Accelerated iterative hard thresholding
Signal Processing
Greed is good: algorithmic results for sparse approximation
IEEE Transactions on Information Theory
Hard Thresholding Pursuit: An Algorithm for Compressive Sensing
SIAM Journal on Numerical Analysis
Hi-index | 0.00 |
In this paper, we present and analyze a new set of low-rank recovery algorithms for linear inverse problems within the class of hard thresholding methods. We provide strategies on how to set up these algorithms via basic ingredients for different configurations to achieve complexity vs. accuracy tradeoffs. Moreover, we study acceleration schemes via memory-based techniques and randomized, ∈-approximate matrix projections to decrease the computational costs in the recovery process. For most of the configurations, we present theoretical analysis that guarantees convergence under mild problem conditions. Simulation results demonstrate notable performance improvements as compared to state-of-the-art algorithms both in terms of reconstruction accuracy and computational complexity.