Matrix Completion
130 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
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Netflix Prize
CAL500
Latest papers
Projected 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.
Matrix Completion with Convex Optimization and Column Subset Selection
ZAL-NASK/CSMC
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We present two algorithms that implement our Columns Selected Matrix Completion (CSMC) method, each dedicated to a different size problem.
Linear Recursive Feature Machines provably recover low-rank matrices
aradha/lin-rfm
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A possible explanation is that common training algorithms for neural networks implicitly perform dimensionality reduction - a process called feature learning.
Metaheuristic Algorithms in Artificial Intelligence with Applications to Bioinformatics, Biostatistics, Ecology and, the Manufacturing Industries
Nature-inspired metaheuristic algorithms are important components of artificial intelligence, and are increasingly used across disciplines to tackle various types of challenging optimization problems.
Teaching Arithmetic to Small Transformers
lee-ny/teaching_arithmetic
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Even in the complete absence of pretraining, this approach significantly and simultaneously improves accuracy, sample complexity, and convergence speed.
Optimal Low-Rank Matrix Completion: Semidefinite Relaxations and Eigenvector Disjunctions
Low-rank matrix completion consists of computing a matrix of minimal complexity that recovers a given set of observations as accurately as possible.
Matrix tri-factorization over the tropical semiring
We show that triFastSTMF performs similarly to Fast-NMTF in terms of approximation and prediction performance when fitted on the whole network.
Rotation Synchronization via Deep Matrix Factorization
In this paper we address the rotation synchronization problem, where the objective is to recover absolute rotations starting from pairwise ones, where the unknowns and the measures are represented as nodes and edges of a graph, respectively.
Graph Signal Sampling for Inductive One-Bit Matrix Completion: a Closed-form Solution
Inductive one-bit matrix completion is motivated by modern applications such as recommender systems, where new users would appear at test stage with the ratings consisting of only ones and no zeros.
Conditions for Estimation of Sensitivities of Voltage Magnitudes to Complex Power Injections
Therefore, this paper addresses the conditions for estimating sensitivities of voltage magnitudes with respect to complex (active and reactive) electric power injections based on sensor measurements.
