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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 81–90 of 796 papers

Title	Date	Tasks	Status	Score
Algebraic Variety Models for High-Rank Matrix Completion	Mar 28, 2017	ClusteringLow-Rank Matrix Completion	CodeCode Available	5
Distant Supervision for Relation Extraction with Matrix Completion	Jun 1, 2014	Low-Rank Matrix CompletionMatrix Completion	CodeCode Available	5
CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filters	May 22, 2017	Community DetectionDeep Learning	CodeCode Available	5
Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient Descent	Feb 1, 2025	Matrix Completion	CodeCode Available	5
Estimating Missing Data in Temporal Data Streams Using Multi-directional Recurrent Neural Networks	Nov 23, 2017	Matrix CompletionMultivariate Time Series Imputation	CodeCode Available	5
Fast and Sample Efficient Inductive Matrix Completion via Multi-Phase Procrustes Flow	Mar 3, 2018	Matrix Completion	CodeCode Available	5
A Perturbation Bound on the Subspace Estimator from Canonical Projections	Jun 28, 2022	ClusteringMatrix Completion	CodeCode Available	5
Adaptive Matrix Completion for the Users and the Items in Tail	Apr 22, 2019	Collaborative FilteringLow-Rank Matrix Completion	CodeCode Available	5
Causal Inference with Noisy and Missing Covariates via Matrix Factorization	Jun 3, 2018	Causal InferenceMatrix Completion	CodeCode Available	5
Collaborative Filtering with Graph Information: Consistency and Scalable Methods	Dec 1, 2015	Collaborative FilteringLow-Rank Matrix Completion	CodeCode Available	5

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