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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 101–150 of 796 papers

TitleStatusHype
Bayesian Learning for Low-Rank matrix reconstruction—0
Bayesian Low-rank Matrix Completion with Dual-graph Embedding: Prior Analysis and Tuning-free Inference—0
Consistent Collective Matrix Completion under Joint Low Rank Structure—0
Bayesian matrix completion: prior specification—0
PAC-Bayesian Matrix Completion with a Spectral Scaled Student Prior—0
Multi-source Learning via Completion of Block-wise Overlapping Noisy Matrices—0
Binary Matrix Completion Using Unobserved Entries—0
Binary matrix completion with nonconvex regularizers—0
Blind Regression: Nonparametric Regression for Latent Variable Models via Collaborative Filtering—0
BlockEcho: Retaining Long-Range Dependencies for Imputing Block-Wise Missing Data—0
Blocked Collaborative Bandits: Online Collaborative Filtering with Per-Item Budget Constraints—0
Boolean Matrix Factorization and Noisy Completion via Message Passing—0
Bounded Manifold Completion—0
Low-rank matrix completion theory via Plucker coordinates—0
A Riemannian gossip approach to decentralized matrix completion—0
Calibrated Elastic Regularization in Matrix Completion—0
A Fast Matrix-Completion-Based Approach for Recommendation Systems—0
Categorical Matrix Completion—0
Causal Imputation for Counterfactual SCMs: Bridging Graphs and Latent Factor Models—0
Causal Inference with Corrupted Data: Measurement Error, Missing Values, Discretization, and Differential Privacy—0
Annotation Projection-based Representation Learning for Cross-lingual Dependency Parsing—0
Algebraic-Combinatorial Methods for Low-Rank Matrix Completion with Application to Athletic Performance Prediction—0
A Non-convex One-Pass Framework for Generalized Factorization Machine and Rank-One Matrix Sensing—0
CAYLEYNETS: SPECTRAL GRAPH CNNS WITH COMPLEX RATIONAL FILTERS—0
Characterization of the equivalence of robustification and regularization in linear and matrix regression—0
Clipped Matrix Completion: A Remedy for Ceiling Effects—0
Adaptive Noisy Matrix Completion—0
A Rank-Corrected Procedure for Matrix Completion with Fixed Basis Coefficients—0
A Proximal Modified Quasi-Newton Method for Nonsmooth Regularized Optimization—0
A Latent Feature Analysis-based Approach for Spatio-Temporal Traffic Data Recovery—0
Accelerated Stochastic Gradient for Nonnegative Tensor Completion and Parallel Implementation—0
A privacy-preserving distributed credible evidence fusion algorithm for collective decision-making—0
Computational Efficient Informative Nonignorable Matrix Completion: A Row- and Column-Wise Matrix U-Statistic Pseudo-Likelihood Approach—0
A Primal-Dual Analysis of Global Optimality in Nonconvex Low-Rank Matrix Recovery—0
A Pre-training Oracle for Predicting Distances in Social Networks—0
Approximating Concavely Parameterized Optimization Problems—0
A Harmonic Extension Approach for Collaborative Ranking—0
Adaptive Multinomial Matrix Completion—0
Conic Descent and its Application to Memory-efficient Optimization over Positive Semidefinite Matrices—0
Community Detection and Matrix Completion with Social and Item Similarity Graphs—0
Communication-Efficient Projection-Free Algorithm for Distributed Optimization—0
Approximate Method of Variational Bayesian Matrix Factorization/Completion with Sparse Prior—0
Completing Any Low-rank Matrix, Provably—0
Completing Low-Rank Matrices with Corrupted Samples from Few Coefficients in General Basis—0
Communication Efficient Parallel Algorithms for Optimization on Manifolds—0
Combining Explicit and Implicit Regularization for Efficient Learning in Deep Networks—0
Approximate matrix completion based on cavity method—0
Computational Graph Completion—0
Computational Limits for Matrix Completion—0
ACCAMS: Additive Co-Clustering to Approximate Matrices Succinctly—0
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