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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 731–740 of 796 papers

TitleStatusHype
Contrastive Matrix Completion with Denoising and Augmented Graph Views for Robust RecommendationCode0
Efficient Model-Based Collaborative Filtering with Fast Adaptive PCACode0
Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient DescentCode0
Implicit Regularization in Deep Matrix FactorizationCode0
A Survey on Nonconvex Regularization Based Sparse and Low-Rank Recovery in Signal Processing, Statistics, and Machine LearningCode0
Preference Completion: Large-scale Collaborative Ranking from Pairwise ComparisonsCode0
Riemannian stochastic variance reduced gradient on Grassmann manifoldCode0
Implicit Regularization in Tensor FactorizationCode0
An Inertial Block Majorization Minimization Framework for Nonsmooth Nonconvex OptimizationCode0
Applications of Nature-Inspired Metaheuristic Algorithms for Tackling Optimization Problems Across DisciplinesCode0
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