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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 576–600 of 796 papers

TitleStatusHype
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
Self-Adaptive Matrix Completion for Heart Rate Estimation From Face Videos Under Realistic Conditions—0
Recognizing Emotions From Abstract Paintings Using Non-Linear Matrix CompletionCode0
Proximal Riemannian Pursuit for Large-Scale Trace-Norm Minimization—0
DeepHand: Robust Hand Pose Estimation by Completing a Matrix Imputed With Deep Features—0
Contextual Bandits with Latent Confounders: An NMF Approach—0
Provable Efficient Online Matrix Completion via Non-convex Stochastic Gradient Descent—0
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