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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 551–575 of 796 papers

TitleStatusHype
Riemannian stochastic variance reduced gradient algorithm with retraction and vector transportCode0
Learning from Ambiguously Labeled Face Images—0
Mutual Kernel Matrix Completion—0
Inductive Pairwise Ranking: Going Beyond the n log(n) Barrier—0
Matrix Completion from O(n) Samples in Linear Time—0
Solving Uncalibrated Photometric Stereo Using Fewer Images by Jointly Optimizing Low-rank Matrix Completion and Integrability—0
Modelling Competitive Sports: Bradley-Terry-Élő Models for Supervised and On-Line Learning of Paired Competition Outcomes—0
Deterministic and Probabilistic Conditions for Finite Completability of Low-rank Multi-View Data—0
Low-Rank Inducing Norms with Optimality InterpretationsCode0
Decentralized Frank-Wolfe Algorithm for Convex and Non-convex Problems—0
A Sparse Interactive Model for Matrix Completion with Side Information—0
Mistake Bounds for Binary Matrix Completion—0
Blind Regression: Nonparametric Regression for Latent Variable Models via Collaborative Filtering—0
High-Rank Matrix Completion and Clustering under Self-Expressive Models—0
Asynchronous Parallel Learning for Neural Networks and Structured Models with Dense Features—0
Distributed Representations for Building Profiles of Users and Items from Text Reviews—0
Noise-Tolerant Life-Long Matrix Completion via Adaptive Sampling—0
A Unified Convex Surrogate for the Schatten-p Norm—0
Using Empirical Covariance Matrix in Enhancing Prediction Accuracy of Linear Models with Missing Information—0
Preference Completion from Partial Rankings—0
Prognostics of Surgical Site Infections using Dynamic Health Data—0
Temporal Matrix Completion with Locally Linear Latent Factors for Medical Applications—0
Dynamic matrix recovery from incomplete observations under an exact low-rank constraint—0
Going off the Grid: Iterative Model Selection for Biclustered Matrix Completion—0
A Unified Computational and Statistical Framework for Nonconvex Low-Rank Matrix Estimation—0
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