Papers Matrix Completion
“Matrix Completion” 태그가 달린 논문 796편 · 필터 해제
Matrix Completion via Nonsmooth Regularization of Fully Connected Neural Networks
Conventional matrix completion methods approximate the missing values by assuming the matrix to be low-rank, which leads to a linear approximation of missing values. It has been shown that enhanced performance could be a…
Matrix CompletionMissing ValuesProjected Gradient Descent for Spectral Compressed Sensing via Symmetric Hankel Factorization
Current spectral compressed sensing methods via Hankel matrix completion employ symmetric factorization to demonstrate the low-rank property of the Hankel matrix. However, previous non-convex gradient methods only utiliz…
compressed sensingMatrix CompletionSensor Network Localization via Riemannian Conjugate Gradient and Rank Reduction: An Extended Version
This paper addresses the Sensor Network Localization (SNL) problem using received signal strength. The SNL is formulated as an Euclidean Distance Matrix Completion (EDMC) problem under the unit ball sample model. Using t…
Matrix CompletionCollaborative 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. The mixed quantization…
Matrix CompletionQuantizationPower-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. Matrix completion state estimators have been suggested, but their computational times could be p…
Matrix CompletionState EstimationImproving Matrix Completion by Exploiting Rating Ordinality in Graph Neural Networks
Matrix completion is an important area of research in recommender systems. Recent methods view a rating matrix as a user-item bi-partite graph with labeled edges denoting observed ratings and predict the edges between th…
Graph Neural NetworkMatrix CompletionRecommendation SystemsRandomized Approach to Matrix Completion: Applications in Collaborative Filtering and Image Inpainting
We present a novel method for matrix completion, specifically designed for matrices where one dimension significantly exceeds the other. Our Columns Selected Matrix Completion (CSMC) method combines Column Subset Selecti…
Collaborative FilteringImage InpaintingLow-Rank Matrix CompletionMatrix Completion+2BlockEcho: Retaining Long-Range Dependencies for Imputing Block-Wise Missing Data
Block-wise missing data poses significant challenges in real-world data imputation tasks. Compared to scattered missing data, block-wise gaps exacerbate adverse effects on subsequent analytic and machine learning tasks, …
ImputationMatrix CompletionEntry-Specific Bounds for Low-Rank Matrix Completion under Highly Non-Uniform Sampling
Low-rank matrix completion concerns the problem of estimating unobserved entries in a matrix using a sparse set of observed entries. We consider the non-uniform setting where the observed entries are sampled with highly …
Low-Rank Matrix CompletionMatrix CompletionDiscovering Abstract Symbolic Relations by Learning Unitary Group Representations
We investigate a principled approach for symbolic operation completion (SOC), a minimal task for studying symbolic reasoning. While conceptually similar to matrix completion, SOC poses a unique challenge in modeling abst…
Inductive BiasMatrix CompletionCausal Imputation for Counterfactual SCMs: Bridging Graphs and Latent Factor Models
We consider the task of causal imputation, where we aim to predict the outcomes of some set of actions across a wide range of possible contexts. As a running example, we consider predicting how different drugs affect cel…
counterfactualImputationMatrix CompletionDoubly 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. The proposed estimator is doubly robust, c…
ImputationMatrix CompletionConvergence of Gradient Descent with Small Initialization for Unregularized Matrix Completion
We study the problem of symmetric matrix completion, where the goal is to reconstruct a positive semidefinite matrix $\rm{X}^\star \in \mathbb{R}^{d\times d}$ of rank-$r$, parameterized by $\rm{U}\rm{U}^{\top}$, from onl…
Matrix CompletionHigh 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 cr…
Matrix CompletionMixed 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. In this work, we consider recovering a mixed dataframe matrix, obtained by complex survey samplin…
Matrix CompletionNutritionSurveySurvey SamplingData-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. This convex optimization problem enables concurrent regularizatio…
Matrix CompletionModel Selectionparameter estimationvalid+1Fast 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. A recently proposed linear model for sparse matrix completion has demonstrated surpris…
Drug DiscoveryMatrix CompletionProduct RecommendationRecommendation SystemsOn the Robustness of Cross-Concentrated Sampling for Matrix Completion
Matrix completion is one of the crucial tools in modern data science research. Recently, a novel sampling model for matrix completion coined cross-concentrated sampling (CCS) has caught much attention. However, the robus…
Matrix CompletionMatrix Completion with Hypergraphs:Sharp Thresholds and Efficient Algorithms
This paper considers the problem of completing a rating matrix based on sub-sampled matrix entries as well as observed social graphs and hypergraphs. We show that there exists a \emph{sharp threshold} on the sample proba…
Matrix CompletionEffect of Beampattern on Matrix Completion with Sparse Arrays
We study the problem of noisy sparse array interpolation, where a large virtual array is synthetically generated by interpolating missing sensors using matrix completion techniques that promote low rank. The current unde…
Low-Rank Matrix CompletionMatrix Completion