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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 251–275 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
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
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
Forecasting Nonnegative Time Series via Sliding Mask Method (SMM) and Latent Clustered Forecast (LCF)—0
Fast Exact Matrix Completion: A Unified Optimization Framework for Matrix Completion—0
Collaborative Automotive Radar Sensing via Mixed-Precision Distributed Array Completion—0
Fast low-rank estimation by projected gradient descent: General statistical and algorithmic guarantees—0
Collaborative Filtering in a Non-Uniform World: Learning with the Weighted Trace Norm—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
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