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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 251–300 of 796 papers

TitleStatusHype
Extended Gauss-Newton and ADMM-Gauss-Newton Algorithms for Low-Rank Matrix Optimization—0
Factor Group-Sparse Regularization for Efficient Low-Rank Matrix Recovery—0
Factorization Approach for Low-complexity Matrix Completion Problems: Exponential Number of Spurious Solutions and Failure of Gradient Methods—0
Factorizing LambdaMART for cold start recommendations—0
Graph Regularized Probabilistic Matrix Factorization for Drug-Drug Interactions Prediction—0
Graph Sampling for Matrix Completion Using Recurrent Gershgorin Disc Shift—0
Collaborative Self-Attention for Recommender Systems—0
A Novel Plug-and-Play Approach for Adversarially Robust Generalization—0
A generalised log-determinant regularizer for online semi-definite programming and its applications—0
Collaborative Filtering in a Non-Uniform World: Learning with the Weighted Trace Norm—0
Fast low-rank estimation by projected gradient descent: General statistical and algorithmic guarantees—0
A Novel Approach to Quantized Matrix Completion Using Huber Loss Measure—0
Fast Low-Rank Bayesian Matrix Completion with Hierarchical Gaussian Prior Models—0
Fast Exact Matrix Completion with Finite Samples—0
Collaborative Filtering and Multi-Label Classification with Matrix Factorization—0
ACCAMS: Additive Co-Clustering to Approximate Matrices Succinctly—0
Fast matrix completion without the condition number—0
Fast methods for denoising matrix completion formulations, with applications to robust seismic data interpolation—0
Fast Methods for Recovering Sparse Parameters in Linear Low Rank Models—0
Fast Optimization Algorithm on Riemannian Manifolds and Its Application in Low-Rank Representation—0
Fast Two-photon Microscopy by Neuroimaging with Oblong Random Acquisition (NORA)—0
Fine-grained Generalization Analysis of Inductive Matrix Completion—0
Results on the algebraic matroid of the determinantal variety—0
Fitting Spectral Decay with the k-Support Norm—0
Fixed-rank matrix factorizations and Riemannian low-rank optimization—0
Flat minima generalize for low-rank matrix recovery—0
Flexible Low-Rank Statistical Modeling with Side Information—0
Column _2,0-norm regularized factorization model of low-rank matrix recovery and its computation—0
Forecasting Nonnegative Time Series via Sliding Mask Method (SMM) and Latent Clustered Forecast (LCF)—0
Functional correspondence by matrix completion—0
Fundamental Conditions for Low-CP-Rank Tensor Completion—0
Fusion Subspace Clustering: Full and Incomplete Data—0
Generalization Bounds for Inductive Matrix Completion in Low-noise Settings—0
Generalization error bounds for kernel matrix completion and extrapolation—0
Generalized Conditional Gradient for Sparse Estimation—0
Generalized Low-Rank Matrix Completion Model with Overlapping Group Error Representation—0
Fast Exact Matrix Completion: A Unified Optimization Framework for Matrix Completion—0
Completing Any Low-rank Matrix, Provably—0
Geometric Inference for General High-Dimensional Linear Inverse Problems—0
Completing Low-Rank Matrices with Corrupted Samples from Few Coefficients in General Basis—0
Collaborative Automotive Radar Sensing via Mixed-Precision Distributed Array Completion—0
Geometric Matrix Completion with Deep Conditional Random Fields—0
Gradient Descent for Sparse Rank-One Matrix Completion for Crowd-Sourced Aggregation of Sparsely Interacting Workers—0
Coherence and sufficient sampling densities for reconstruction in compressed sensing—0
Faster Convergence of Riemannian Stochastic Gradient Descent with Increasing Batch Size—0
Global Convergence of Stochastic Gradient Descent for Some Non-convex Matrix Problems—0
A note on the statistical view of matrix completion—0
Computational Graph Completion—0
Going off the Grid: Iterative Model Selection for Biclustered Matrix Completion—0
Graph-Based Matrix Completion Applied to Weather Data—0
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