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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 551–600 of 796 papers

TitleStatusHype
Exact tensor completion with sum-of-squares—0
SAR: Semantic Analysis for Recommendation—0
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
Blind Regression: Nonparametric Regression for Latent Variable Models via Collaborative Filtering—0
Asynchronous Parallel Learning for Neural Networks and Structured Models with Dense Features—0
A Sparse Interactive Model for Matrix Completion with Side Information—0
Noise-Tolerant Life-Long Matrix Completion via Adaptive Sampling—0
Distributed Representations for Building Profiles of Users and Items from Text Reviews—0
High-Rank Matrix Completion and Clustering under Self-Expressive Models—0
Mistake Bounds for Binary Matrix Completion—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
Spectral Inference Methods on Sparse Graphs: Theory and Applications—0
Unorganized Malicious Attacks Detection—0
Low-tubal-rank Tensor Completion using Alternating Minimization—0
Max-Norm Optimization for Robust Matrix Recovery—0
Tensor Completion by Alternating Minimization under the Tensor Train (TT) Model—0
Noisy Inductive Matrix Completion Under Sparse Factor Models—0
Robust Spectral Detection of Global Structures in the Data by Learning a Regularization—0
Tracking Completion—0
A Non-convex One-Pass Framework for Generalized Factorization Machine and Rank-One Matrix Sensing—0
Which Tumblr Post Should I Read Next?—0
Learning of Generalized Low-Rank Models: A Greedy Approach—0
Fast Methods for Recovering Sparse Parameters in Linear Low Rank Models—0
Hybrid Recommender System based on AutoencodersCode0
Nearly-optimal Robust Matrix Completion—0
Link Prediction via Matrix Completion—0
Generalizing to Unseen Entities and Entity Pairs with Row-less Universal SchemaCode0
Efficient Pairwise Learning Using Kernel Ridge Regression: an Exact Two-Step Method—0
Extended Gauss-Newton and ADMM-Gauss-Newton Algorithms for Low-Rank Matrix Optimization—0
Scalable Algorithms for Tractable Schatten Quasi-Norm Minimization—0
DeepHand: Robust Hand Pose Estimation by Completing a Matrix Imputed With Deep Features—0
Proximal Riemannian Pursuit for Large-Scale Trace-Norm Minimization—0
Contextual Bandits with Latent Confounders: An NMF Approach—0
Self-Adaptive Matrix Completion for Heart Rate Estimation From Face Videos Under Realistic Conditions—0
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