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
Benchmarks
These leaderboards are used to track progress in Matrix Completion
Libraries
Use these libraries to find Matrix Completion models and implementations
Netflix Prize
CAL500
Latest papers with no code
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.
Causal Imputation for Counterfactual SCMs: Bridging Graphs and Latent Factor Models
We study the index-only setting, where the actions and contexts are categorical variables with a finite number of possible values.
Doubly Robust Inference in Causal Latent Factor Models
This article introduces a new estimator of average treatment effects under unobserved confounding in modern data-rich environments featuring large numbers of units and outcomes.
Convergence of Gradient Descent with Small Initialization for Unregularized Matrix Completion
In the over-parameterized regime where $r'\geq r$, we show that, with $\widetilde\Omega(dr^9)$ observations, GD with an initial point $\|\rm{U}_0\| \leq \epsilon$ converges near-linearly to an $\epsilon$-neighborhood of $\rm{X}^\star$.
High Dimensional Factor Analysis with Weak Factors
This paper studies the principal components (PC) estimator for high dimensional approximate factor models with weak factors in that the factor loading ($\boldsymbol{\Lambda}^0$) scales sublinearly in the number $N$ of cross-section units, i. e., $\boldsymbol{\Lambda}^{0\top} \boldsymbol{\Lambda}^0 / N^\alpha$ is positive definite in the limit for some $\alpha \in (0, 1)$.
Mixed Matrix Completion in Complex Survey Sampling under Heterogeneous Missingness
Modern surveys with large sample sizes and growing mixed-type questionnaires require robust and scalable analysis methods.
Data-driven model selection within the matrix completion method for causal panel data models
Matrix completion estimators are employed in causal panel data models to regulate the rank of the underlying factor model using nuclear norm minimization.
Fast Dual-Regularized Autoencoder for Sparse Biological Data
Relationship inference from sparse data is an important task with applications ranging from product recommendation to drug discovery.
On the Robustness of Cross-Concentrated Sampling for Matrix Completion
Matrix completion is one of the crucial tools in modern data science research.
