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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 91–100 of 158 papers

TitleStatusHype
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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