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Low-Rank Matrix Completion

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

Papers

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Showing 101–125 of 158 papers

TitleStatusHype
Recovery of damped exponentials using structured low rank matrix completion—0
Reflection Removal Using Low-Rank Matrix Completion—0
Relaxed Leverage Sampling for Low-rank Matrix Completion—0
Reproducing Kernel Hilbert Space, Mercer's Theorem, Eigenfunctions, Nyström Method, and Use of Kernels in Machine Learning: Tutorial and Survey—0
Riemannian Optimization for Non-convex Euclidean Distance Geometry with Global Recovery Guarantees—0
Riemannian Stochastic Proximal Gradient Methods for Nonsmooth Optimization over the Stiefel Manifold—0
Riemannian stochastic quasi-Newton algorithm with variance reduction and its convergence analysis—0
Robust Low-rank Matrix Completion via an Alternating Manifold Proximal Gradient Continuation Method—0
Robust Low-Rank Matrix Completion via a New Sparsity-Inducing Regularizer—0
Robust Matrix Completion with Heavy-tailed Noise—0
RTRMC: A Riemannian trust-region method for low-rank matrix completion—0
Scaled Gradients on Grassmann Manifolds for Matrix Completion—0
Scaled stochastic gradient descent for low-rank matrix completion—0
Secrets of Matrix Factorization: Approximations, Numerics, Manifold Optimization and Random Restarts—0
Solving Uncalibrated Photometric Stereo Using Fewer Images by Jointly Optimizing Low-rank Matrix Completion and Integrability—0
Sparse Array Beamformer Design for Active and Passive Sensing—0
Sparse Group Inductive Matrix Completion—0
Static and Dynamic Robust PCA and Matrix Completion: A Review—0
Structured low-rank matrix completion for forecasting in time series analysis—0
Structure-Preserving Progressive Low-rank Image Completion for Defending Adversarial Attacks—0
Symmetric Matrix Completion with ReLU Sampling—0
Symmetric Tensor Completion from Multilinear Entries and Learning Product Mixtures over the Hypercube—0
Tensor Methods for Nonlinear Matrix Completion—0
The Algebraic Combinatorial Approach for Low-Rank Matrix Completion—0
Leave-one-out Approach for Matrix Completion: Primal and Dual Analysis—0
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