Matrix Completion
131 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
Concentration properties of fractional posterior in 1-bit matrix completion
The problem of estimating a matrix based on a set of its observed entries is commonly referred to as the matrix completion problem.
Energy-modified Leverage Sampling for Radio Map Construction via Matrix Completion
This paper explores an energy-modified leverage sampling strategy for matrix completion in radio map construction.
Statistical Inference For Noisy Matrix Completion Incorporating Auxiliary Information
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
As such, the resulting regularized objective function becomes nonsmooth and nonconvex, i. e., existing gradient-based methods cannot be applied to our model.
Sensor Network Localization via Riemannian Conjugate Gradient and Rank Reduction: An Extended Version
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
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
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
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
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
Our bounds characterize the hardness of estimating each entry as a function of the localized sampling probabilities.
