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Matrix Completion

Matrix Completion is a method for recovering lost information. It originates from machine learning and usually deals with highly sparse matrices. Missing or unknown data is estimated using the low-rank matrix of the known data.

Source: A Fast Matrix-Completion-Based Approach for Recommendation Systems

Papers

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Showing 526–550 of 796 papers

TitleStatusHype
Efficient Low Rank Tensor Ring Completion—0
Recommendation via matrix completion using Kolmogorov complexity—0
Reflection Removal Using Low-Rank Matrix Completion—0
Concentration of tempered posteriors and of their variational approximations—0
Reexamining Low Rank Matrix Factorization for Trace Norm Regularization—0
Crowdsourcing with Sparsely Interacting Workers—0
Empirical Bayes Matrix Completion—0
CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral FiltersCode0
A Non-monotone Alternating Updating Method for A Class of Matrix Factorization Problems—0
Representation learning of drug and disease terms for drug repositioning—0
Matrix Completion via Factorizing Polynomials—0
Matrix completion with queries—0
A Riemannian gossip approach to subspace learning on Grassmann manifold—0
Targeted matrix completion—0
Matrix Completion and Related Problems via Strong Duality—0
Structured low-rank matrix learning: algorithms and applications—0
Recovery of damped exponentials using structured low rank matrix completion—0
No Spurious Local Minima in Nonconvex Low Rank Problems: A Unified Geometric Analysis—0
Fundamental Conditions for Low-CP-Rank Tensor Completion—0
Novel Structured Low-rank algorithm to recover spatially smooth exponential image time series—0
Algebraic Variety Models for High-Rank Matrix CompletionCode0
Spectrum Estimation from a Few Entries—0
Riemannian stochastic quasi-Newton algorithm with variance reduction and its convergence analysis—0
Accelerating Permutation Testing in Voxel-wise Analysis through Subspace Tracking: A new plugin for SnPM—0
On the Power of Truncated SVD for General High-rank Matrix Estimation Problems—0
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