Matrix Completion
129 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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Libraries
Use these libraries to find Matrix Completion models and implementationsMost implemented papers
Low-Rank Inducing Norms with Optimality Interpretations
A posteriori guarantees on solving an underlying rank constrained optimization problem with these convex relaxations are provided.
Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks
Matrix completion models are among the most common formulations of recommender systems.
CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filters
The rise of graph-structured data such as social networks, regulatory networks, citation graphs, and functional brain networks, in combination with resounding success of deep learning in various applications, has brought the interest in generalizing deep learning models to non-Euclidean domains.
Estimating Missing Data in Temporal Data Streams Using Multi-directional Recurrent Neural Networks
Existing methods address this estimation problem by interpolating within data streams or imputing across data streams (both of which ignore important information) or ignoring the temporal aspect of the data and imposing strong assumptions about the nature of the data-generating process and/or the pattern of missing data (both of which are especially problematic for medical data).
Indian Regional Movie Dataset for Recommender Systems
It consists of movies belonging to 18 different Indian regional languages and metadata of users with varying demographics.
Accelerating Ill-Conditioned Low-Rank Matrix Estimation via Scaled Gradient Descent
Low-rank matrix estimation is a canonical problem that finds numerous applications in signal processing, machine learning and imaging science.
Matrix Completion with Quantified Uncertainty through Low Rank Gaussian Copula
The time required to fit the model scales linearly with the number of rows and the number of columns in the dataset.
Compressed sensing of low-rank plus sparse matrices
This manuscript develops similar guarantees showing that $m\times n$ matrices that can be expressed as the sum of a rank-$r$ matrix and a $s$-sparse matrix can be recovered by computationally tractable methods from $\mathcal{O}(r(m+n-r)+s)\log(mn/s)$ linear measurements.
A regularized deep matrix factorized model of matrix completion for image restoration
In this work, we propose a Regularized Deep Matrix Factorized (RDMF) model for image restoration, which utilizes the implicit bias of the low rank of deep neural networks and the explicit bias of total variation.
Inductive Matrix Completion Using Graph Autoencoder
However, without node content (i. e., side information) for training, the user (or item) specific representation can not be learned in the inductive setting, that is, a model trained on one group of users (or items) cannot adapt to new users (or items).