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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 376–400 of 796 papers

TitleStatusHype
Scalable Bayesian Non-linear Matrix Completion—0
Deep Non-Rigid Structure from Motion with Missing Data—0
Collaborative Filtering and Multi-Label Classification with Matrix Factorization—0
The Landscape of Non-convex Empirical Risk with Degenerate Population Risk—0
Low-rank matrix completion and denoising under Poisson noise—0
A divide-and-conquer algorithm for binary matrix completion—0
Depth Restoration: A fast low-rank matrix completion via dual-graph regularization—0
Approximate matrix completion based on cavity method—0
Online Variational Bayesian Subspace Filtering with Applications—0
Generalization error bounds for kernel matrix completion and extrapolation—0
Online Matrix Completion with Side Information—0
Efficiently escaping saddle points on manifolds—0
Inference and Uncertainty Quantification for Noisy Matrix Completion—0
Graphon Estimation from Partially Observed Network DataCode0
Guaranteed Matrix Completion Under Multiple Linear Transformations—0
Implicit Regularization in Deep Matrix FactorizationCode0
Spectral Perturbation Meets Incomplete Multi-view Data—0
Sum-of-squares meets square loss: Fast rates for agnostic tensor completion—0
Matrix Completion in the Unit Hypercube via Structured Matrix FactorizationCode0
STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender SystemsCode0
Collaborative Self-Attention for Recommender Systems—0
Prediction with Unpredictable Feature Evolution—0
Inductive Matrix Completion Based on Graph Neural NetworksCode1
Gradient Descent for Sparse Rank-One Matrix Completion for Crowd-Sourced Aggregation of Sparsely Interacting Workers—0
Adaptive Matrix Completion for the Users and the Items in TailCode0
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