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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 251–275 of 796 papers

TitleStatusHype
WARPd: A linearly convergent first-order method for inverse problems with approximate sharpness conditionsCode0
Uncertainty Quantification For Low-Rank Matrix Completion With Heterogeneous and Sub-Exponential Noise—0
Projection-Free Algorithm for Stochastic Bi-level Optimization—0
Computational Graph Completion—0
Reconstruction of Fragmented Trajectories of Collective Motion using Hadamard Deep Autoencoders—0
Factorization Approach for Low-complexity Matrix Completion Problems: Exponential Number of Spurious Solutions and Failure of Gradient Methods—0
One-Bit Matrix Completion with Differential Privacy—0
Multi-way Clustering and Discordance Analysis through Deep Collective Matrix Tri-Factorization—0
Provable Low Rank Plus Sparse Matrix Separation Via Nonconvex Regularizers—0
Weighted Low Rank Matrix Approximation and Acceleration—0
Accelerated Stochastic Gradient for Nonnegative Tensor Completion and Parallel Implementation—0
On the Fundamental Limits of Matrix Completion: Leveraging Hierarchical Similarity Graphs—0
Matrix Completion of World Trade—0
Provable Tensor-Train Format Tensor Completion by Riemannian Optimization—0
Attribute-based Explanations of Non-Linear Embeddings of High-Dimensional Data—0
Private Alternating Least Squares: Practical Private Matrix Completion with Tighter Rates—0
Causal Inference with Corrupted Data: Measurement Error, Missing Values, Discretization, and Differential Privacy—0
Low Rank Quaternion Matrix Recovery via Logarithmic Approximation—0
Global Convergence of Gradient Descent for Asymmetric Low-Rank Matrix Factorization—0
GNMR: A provable one-line algorithm for low rank matrix recoveryCode0
Matrix Completion with Model-free Weighting—0
A Pre-training Oracle for Predicting Distances in Social Networks—0
Patch Tracking-based Streaming Tensor Ring Completion for Visual Data Recovery—0
Multi-source Learning via Completion of Block-wise Overlapping Noisy Matrices—0
Low-Rank Hankel Tensor Completion for Traffic Speed EstimationCode0
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