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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 651–700 of 796 papers

TitleStatusHype
Annotation Projection-based Representation Learning for Cross-lingual Dependency Parsing—0
Notes on Low-rank Matrix Factorization—0
Completing Low-Rank Matrices with Corrupted Samples from Few Coefficients in General Basis—0
Taming the Wild: A Unified Analysis of Hogwild!-Style Algorithms—0
On the properties of variational approximations of Gibbs posteriors—0
Matrix Completion from Fewer Entries: Spectral Detectability and Rank Estimation—0
Image Tag Completion and Refinement by Subspace Clustering and Matrix Completion—0
Symmetric Tensor Completion from Multilinear Entries and Learning Product Mixtures over the Hypercube—0
Matrix Completion for Resolving Label Ambiguity—0
A New Retraction for Accelerating the Riemannian Three-Factor Low-Rank Matrix Completion Algorithm—0
Smooth PARAFAC Decomposition for Tensor Completion—0
Towards Faster Rates and Oracle Property for Low-Rank Matrix Estimation—0
Optimal Low-Rank Tensor Recovery from Separable Measurements: Four Contractions Suffice—0
Prediction and Quantification of Individual Athletic Performance—0
Relative Error Bound Analysis for Nuclear Norm Regularized Matrix Completion—0
Social Trust Prediction via Max-norm Constrained 1-bit Matrix Completion—0
Regularization-free estimation in trace regression with symmetric positive semidefinite matrices—0
Poisson Matrix Recovery and Completion—0
Streaming, Memory Limited Matrix Completion with Noise—0
Structured Matrix Completion with Applications to Genomic Data Integration—0
Relaxed Leverage Sampling for Low-rank Matrix Completion—0
Online Matrix Completion and Online Robust PCA—0
Scalable Nuclear-norm Minimization by Subspace Pursuit Proximal Riemannian Gradient—0
A Characterization of Deterministic Sampling Patterns for Low-Rank Matrix Completion—0
Matrix Completion with Noisy Entries and Outliers—0
1-Bit Matrix Completion under Exact Low-Rank Constraint—0
Low Rank Matrix Completion with Exponential Family Noise—0
Exact tensor completion using t-SVD—0
Speeding up Permutation Testing in Neuroimaging—0
Recovery of Piecewise Smooth Images from Few Fourier Samples—0
Poisson Matrix Completion—0
Noisy Tensor Completion via the Sum-of-Squares Hierarchy—0
Bayesian Learning for Low-Rank matrix reconstruction—0
ACCAMS: Additive Co-Clustering to Approximate Matrices Succinctly—0
Learning Parameters for Weighted Matrix Completion via Empirical Estimation—0
Functional correspondence by matrix completion—0
Adjusting Leverage Scores by Row Weighting: A Practical Approach to Coherent Matrix Completion—0
Quantized Matrix Completion for Personalized Learning—0
Probabilistic low-rank matrix completion on finite alphabets—0
Consistent Collective Matrix Completion under Joint Low Rank Structure—0
Spectral k-Support Norm Regularization—0
Deterministic Symmetric Positive Semidefinite Matrix Completion—0
Online Optimization for Max-Norm Regularization—0
Guaranteed Matrix Completion via Non-convex Factorization—0
Signal Recovery on Graphs: Variation Minimization—0
PU Learning for Matrix Completion—0
Characterization of the equivalence of robustification and regularization in linear and matrix regression—0
Maximum Entropy Kernels for System Identification—0
Errata: Distant Supervision for Relation Extraction with Matrix Completion—0
Global Convergence of Stochastic Gradient Descent for Some Non-convex Matrix Problems—0
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