Results 81 to 90 of about 3,678,430 (211)
Matrix completion by singular value thresholding: sharp bounds
, 2015 We consider the matrix completion problem where the aim is to esti-mate a large data matrix for which only a relatively small random subset of its entries is observed.
Klopp, Olga
core +2 more sources
Toeplitz matrix completion via a low-rank approximation algorithm
Journal of Inequalities and Applications, 2020 In this paper, we propose a low-rank matrix approximation algorithm for solving the Toeplitz matrix completion (TMC) problem. The approximation matrix was obtained by the mean projection operator on the set of feasible Toeplitz matrices for every ...
Ruiping Wen, Yaru Fu
doaj +1 more source
Graph Convolutional Matrix Completion [PDF]
, 2017 We consider matrix completion for recommender systems from the point of view of link prediction on graphs. Interaction data such as movie ratings can be represented by a bipartite user-item graph with labeled edges denoting observed ratings.
Berg, Rianne van den +2 more
core +2 more sources
Fast Low-Rank Bayesian Matrix Completion with Hierarchical Gaussian Prior Models
, 2017 The problem of low rank matrix completion is considered in this paper. To exploit the underlying low-rank structure of the data matrix, we propose a hierarchical Gaussian prior model, where columns of the low-rank matrix are assumed to follow a Gaussian ...
Duan, Huiping +4 more
core +1 more source
Online Matrix Completion and Online Robust PCA
, 2015 This work studies two interrelated problems - online robust PCA (RPCA) and online low-rank matrix completion (MC). In recent work by Cand\`{e}s et al., RPCA has been defined as a problem of separating a low-rank matrix (true data), $L:=[\ell_1, \ell_2 ...
Lois, Brian, Vaswani, Namrata
core +1 more source
A Survey of Matrix Completion Methods for Recommendation Systems
Big Data Mining and Analytics, 2018 In recent years, the recommendation systems have become increasingly popular and have been used in a broad variety of applications. Here, we investigate the matrix completion techniques for the recommendation systems that are based on collaborative ...
Andy Ramlatchan +5 more
doaj +1 more source
Fast Exact Matrix Completion: A Unified Optimization Framework for Matrix Completion
, 2019 We formulate the problem of matrix completion with and without side information as a non-convex optimization problem. We design fastImpute based on non-convex gradient descent and show it converges to a global minimum that is guaranteed to recover closely the underlying matrix while it scales to matrices of sizes beyond $10^5 \times 10^5$.
Bertsimas, D, Li, ML
openaire +3 more sources
A Gradient System for Low Rank Matrix Completion
Axioms, 2018 In this article we present and discuss a two step methodology to find the closest low rank completion of a sparse large matrix. Given a large sparse matrix M, the method consists of fixing the rank to r and then looking for the closest rank-r matrix X to
Carmela Scalone, Nicola Guglielmi
doaj +1 more source
Fast Methods for Recovering Sparse Parameters in Linear Low Rank Models
, 2016 In this paper, we investigate the recovery of a sparse weight vector (parameters vector) from a set of noisy linear combinations. However, only partial information about the matrix representing the linear combinations is available.
Amini, Arash +2 more
core +1 more source
Structured matrix estimation and completion [PDF]
Bernoulli, 2019 We study the problem of matrix estimation and matrix completion under a general framework. This framework includes several important models as special cases such as the gaussian mixture model, mixed membership model, bi-clustering model and dictionary learning.
Klopp, Olga +3 more
openaire +3 more sources