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# Low-Rank Matrix Completion Edit

6 papers with code · Methodology
Subtask of Matrix Completion

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# Low-rank matrix completion and denoising under Poisson noise

11 Jul 2019

This paper considers the problem of estimating a low-rank matrix from the observation of all, or a subset, of its entries in the presence of Poisson noise.

# A divide-and-conquer algorithm for binary matrix completion

9 Jul 2019

We propose an algorithm for low rank matrix completion for matrices with binary entries which obtains explicit binary factors.

# Depth Restoration: A fast low-rank matrix completion via dual-graph regularization

5 Jul 2019

As a real scenes sensing approach, depth information obtains the widespread applications.

# Efficiently escaping saddle points on manifolds

10 Jun 2019

Specifically, for an arbitrary Riemannian manifold $\mathcal{M}$ of dimension $d$, a sufficiently smooth (possibly non-convex) objective function $f$, and under weak conditions on the retraction chosen to move on the manifold, with high probability, our version of PRGD produces a point with gradient smaller than $\epsilon$ and Hessian within $\sqrt{\epsilon}$ of being positive semidefinite in $O((\log{d})^4 / \epsilon^{2})$ gradient queries.

# Guaranteed Matrix Completion Under Multiple Linear Transformations

Low-rank matrix completion (LRMC) is a classical model in both computer vision (CV) and machine learning, and has been successfully applied to various real applications.

# Adaptive Matrix Completion for the Users and the Items in Tail

22 Apr 2019

In this work, we show that the skewed distribution of ratings in the user-item rating matrix of real-world datasets affects the accuracy of matrix-completion-based approaches.

# Noisy Matrix Completion: Understanding Statistical Guarantees for Convex Relaxation via Nonconvex Optimization

20 Feb 2019

This paper studies noisy low-rank matrix completion: given partial and corrupted entries of a large low-rank matrix, the goal is to estimate the underlying matrix faithfully and efficiently.

# Double Weighted Truncated Nuclear Norm Regularization for Low-Rank Matrix Completion

7 Jan 2019

The truncated nuclear norm regularization (TNNR) method is applicable in real-world scenarios.

# Communication Efficient Parallel Algorithms for Optimization on Manifolds

Our work aims to fill a critical gap in the literature by generalizing parallel inference algorithms to optimization on manifolds.

# Communication Efficient Parallel Algorithms for Optimization on Manifolds

Our work aims to fill a critical gap in the literature by generalizing parallel inference algorithms to optimization on manifolds.