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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 76–100 of 158 papers

TitleStatusHype
Low-rank matrix completion and denoising under Poisson noise—0
A divide-and-conquer algorithm for binary matrix completion—0
Depth Restoration: A fast low-rank matrix completion via dual-graph regularization—0
Efficiently escaping saddle points on manifolds—0
Guaranteed Matrix Completion Under Multiple Linear Transformations—0
Adaptive Matrix Completion for the Users and the Items in TailCode0
Simple Heuristics Yield Provable Algorithms for Masked Low-Rank Approximation—0
Noisy Matrix Completion: Understanding Statistical Guarantees for Convex Relaxation via Nonconvex Optimization—0
Double Weighted Truncated Nuclear Norm Regularization for Low-Rank Matrix Completion—0
Communication Efficient Parallel Algorithms for Optimization on Manifolds—0
Provable Subspace Tracking from Missing Data and Matrix CompletionCode0
Fusion Subspace Clustering: Full and Incomplete Data—0
Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval and Matrix Completion—0
Ranking Recovery from Limited Comparisons using Low-Rank Matrix Completion—0
Sparse Group Inductive Matrix Completion—0
Tensor Methods for Nonlinear Matrix Completion—0
Exact Reconstruction of Euclidean Distance Geometry Problem Using Low-rank Matrix Completion—0
Leave-one-out Approach for Matrix Completion: Primal and Dual Analysis—0
Static and Dynamic Robust PCA and Matrix Completion: A Review—0
Structured low-rank matrix completion for forecasting in time series analysis—0
Learning Latent Features with Pairwise Penalties in Low-Rank Matrix Completion—0
On Deterministic Sampling Patterns for Robust Low-Rank Matrix Completion—0
Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval, Matrix Completion, and Blind Deconvolution—0
Background Subtraction via Fast Robust Matrix Completion—0
Fast Low-Rank Bayesian Matrix Completion with Hierarchical Gaussian Prior Models—0
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