Matrix Completion

129 papers with code • 0 benchmarks • 4 datasets

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

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Latest papers with no code

Statistical Inference For Noisy Matrix Completion Incorporating Auxiliary Information

no code yet • 22 Mar 2024

This paper investigates statistical inference for noisy matrix completion in a semi-supervised model when auxiliary covariates are available.

Matrix Completion via Nonsmooth Regularization of Fully Connected Neural Networks

no code yet • 15 Mar 2024

As such, the resulting regularized objective function becomes nonsmooth and nonconvex, i. e., existing gradient-based methods cannot be applied to our model.

Projected Gradient Descent for Spectral Compressed Sensing via Symmetric Hankel Factorization

no code yet • 14 Mar 2024

Current spectral compressed sensing methods via Hankel matrix completion employ symmetric factorization to demonstrate the low-rank property of the Hankel matrix.

Sensor Network Localization via Riemannian Conjugate Gradient and Rank Reduction: An Extended Version

no code yet • 13 Mar 2024

The SNL is formulated as an Euclidean Distance Matrix Completion (EDMC) problem under the unit ball sample model.

Collaborative Automotive Radar Sensing via Mixed-Precision Distributed Array Completion

no code yet • 13 Mar 2024

This paper investigates the effects of coarse quantization with mixed precision on measurements obtained from sparse linear arrays, synthesized by a collaborative automotive radar sensing strategy.

Power-Flow-Embedded Projection Conic Matrix Completion for Low-Observable Distribution Systems

no code yet • 8 Mar 2024

A low-observable distribution system has insufficient measurements for conventional weighted least square state estimators.

Improving Matrix Completion by Exploiting Rating Ordinality in Graph Neural Networks

no code yet • 7 Mar 2024

We introduce a new method, called ROGMC, to leverage Rating Ordinality in GNN-based Matrix Completion.

BlockEcho: Retaining Long-Range Dependencies for Imputing Block-Wise Missing Data

no code yet • 29 Feb 2024

The advantage also holds for scattered missing data at high missing rates.

Entry-Specific Bounds for Low-Rank Matrix Completion under Highly Non-Uniform Sampling

no code yet • 29 Feb 2024

Our bounds characterize the hardness of estimating each entry as a function of the localized sampling probabilities.

Causal Imputation for Counterfactual SCMs: Bridging Graphs and Latent Factor Models

no code yet • 22 Feb 2024

We study the index-only setting, where the actions and contexts are categorical variables with a finite number of possible values.