SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 741–750 of 796 papers

TitleStatusHype
Meta-learning based Alternating Minimization Algorithm for Non-convex OptimizationCode0
Recognizing Emotions From Abstract Paintings Using Non-Linear Matrix CompletionCode0
Imputation and low-rank estimation with Missing Not At Random dataCode0
Conditions for Estimation of Sensitivities of Voltage Magnitudes to Complex Power InjectionsCode0
Estimating Missing Data in Temporal Data Streams Using Multi-directional Recurrent Neural NetworksCode0
Riemannian stochastic variance reduced gradient algorithm with retraction and vector transportCode0
Nonnegative Tensor Completion via Integer OptimizationCode0
Indian Regional Movie Dataset for Recommender SystemsCode0
Bounded Simplex-Structured Matrix Factorization: Algorithms, Identifiability and ApplicationsCode0
Training Complex Models with Multi-Task Weak SupervisionCode0
Show:102550
← PrevPage 75 of 80Next →

No leaderboard results yet.