Low-Rank Matrix Completion
25 papers with code • 0 benchmarks • 0 datasets
Low-Rank Matrix Completion is an important problem with several applications in areas such as recommendation systems, sketching, and quantum tomography. The goal in matrix completion is to recover a low rank matrix, given a small number of entries of the matrix.
Source: Universal Matrix Completion
Benchmarks
These leaderboards are used to track progress in Low-Rank Matrix Completion
Latest papers with no code
Entry-Specific Bounds for Low-Rank Matrix Completion under Highly Non-Uniform Sampling
Our bounds characterize the hardness of estimating each entry as a function of the localized sampling probabilities.
Effect of Beampattern on Matrix Completion with Sparse Arrays
In this paper, we make advances towards solidifying this understanding by revealing the role of the physical beampattern of the sparse array on the performance of low rank matrix completion techniques.
Harmonic Retrieval Using Weighted Lifted-Structure Low-Rank Matrix Completion
In this paper, we investigate the problem of recovering the frequency components of a mixture of $K$ complex sinusoids from a random subset of $N$ equally-spaced time-domain samples.
A framework to generate sparsity-inducing regularizers for enhanced low-rank matrix completion
Applying half-quadratic optimization to loss functions can yield the corresponding regularizers, while these regularizers are usually not sparsity-inducing regularizers (SIRs).
Robust Low-Rank Matrix Completion via a New Sparsity-Inducing Regularizer
Moreover, the closed-form solution to its Moreau envelope, namely, the proximity operator, is derived.
Matrix Completion in Almost-Verification Time
In the well-studied setting where $\mathbf{M}$ has incoherent row and column spans, our algorithms complete $\mathbf{M}$ to high precision from $mr^{2+o(1)}$ observations in $mr^{3 + o(1)}$ time (omitting logarithmic factors in problem parameters), improving upon the prior state-of-the-art [JN15] which used $\approx mr^5$ samples and $\approx mr^7$ time.
Data-based system representations from irregularly measured data
Non-parametric representations of dynamical systems based on the image of a Hankel matrix of data are extensively used for data-driven control.
Non-Convex Optimizations for Machine Learning with Theoretical Guarantee: Robust Matrix Completion and Neural Network Learning
Despite the recent development in machine learning, most learning systems are still under the concept of "black box", where the performance cannot be understood and derived.
Graph-Based Matrix Completion Applied to Weather Data
Low-rank matrix completion is the task of recovering unknown entries of a matrix by assuming that the true matrix admits a good low-rank approximation.
Matrix Completion from General Deterministic Sampling Patterns
Most of the existing works on provable guarantees for low-rank matrix completion algorithms rely on some unrealistic assumptions such that matrix entries are sampled randomly or the sampling pattern has a specific structure.
