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Matrix Completion

31 papers with code · Methodology

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Graph Convolutional Matrix Completion

7 Jun 2017tkipf/gae

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.

COLLABORATIVE FILTERING LINK PREDICTION MATRIX COMPLETION

Matrix Completion and Low-Rank SVD via Fast Alternating Least Squares

9 Oct 2014iskandr/fancyimpute

The matrix-completion problem has attracted a lot of attention, largely as a result of the celebrated Netflix competition. Two popular approaches for solving the problem are nuclear-norm-regularized matrix approximation (Candes and Tao, 2009, Mazumder, Hastie and Tibshirani, 2010), and maximum-margin matrix factorization (Srebro, Rennie and Jaakkola, 2005).

MATRIX COMPLETION

Decomposition into Low-rank plus Additive Matrices for Background/Foreground Separation: A Review for a Comparative Evaluation with a Large-Scale Dataset

4 Nov 2015andrewssobral/lrslibrary

The main goal of these similar problem formulations is to obtain explicitly or implicitly a decomposition into low-rank matrix plus additive matrices. In this context, this work aims to initiate a rigorous and comprehensive review of the similar problem formulations in robust subspace learning and tracking based on decomposition into low-rank plus additive matrices for testing and ranking existing algorithms for background/foreground separation.

MATRIX COMPLETION

CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filters

22 May 2017SeongokRyu/Graph-neural-networks

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. In this paper, we introduce a new spectral domain convolutional architecture for deep learning on graphs.

COMMUNITY DETECTION IMAGE CLASSIFICATION MATRIX COMPLETION NODE CLASSIFICATION

Hybrid Recommender System based on Autoencoders

24 Jun 2016fstrub95/Autoencoders_cf

A standard model for Recommender Systems is the Matrix Completion setting: given partially known matrix of ratings given by users (rows) to items (columns), infer the unknown ratings. In the last decades, few attempts where done to handle that objective with Neural Networks, but recently an architecture based on Autoencoders proved to be a promising approach.

COLLABORATIVE FILTERING MATRIX COMPLETION

Dictionary Learning for Massive Matrix Factorization

3 May 2016arthurmensch/modl

Sparse matrix factorization is a popular tool to obtain interpretable data decompositions, which are also effective to perform data completion or denoising. Its applicability to large datasets has been addressed with online and randomized methods, that reduce the complexity in one of the matrix dimension, but not in both of them.

COLLABORATIVE FILTERING DICTIONARY LEARNING MATRIX COMPLETION

Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks

NeurIPS 2017 fmonti/mgcnn

Matrix completion models are among the most common formulations of recommender systems. Recent works have showed a boost of performance of these techniques when introducing the pairwise relationships between users/items in the form of graphs, and imposing smoothness priors on these graphs.

COLLABORATIVE FILTERING MATRIX COMPLETION

Generalized Low Rank Models

1 Oct 2014madeleineudell/LowRankModels.jl

Principal components analysis (PCA) is a well-known technique for approximating a tabular data set by a low rank matrix. Here, we extend the idea of PCA to handle arbitrary data sets consisting of numerical, Boolean, categorical, ordinal, and other data types.

DENOISING MATRIX COMPLETION

Training Complex Models with Multi-Task Weak Supervision

5 Oct 2018HazyResearch/metal

As machine learning models continue to increase in complexity, collecting large hand-labeled training sets has become one of the biggest roadblocks in practice. Instead, weaker forms of supervision that provide noisier but cheaper labels are often used.

MATRIX COMPLETION

VIGAN: Missing View Imputation with Generative Adversarial Networks

22 Aug 2017chaoshangcs/VIGAN

In many disciplines, data are collected from heterogeneous sources, resulting in multi-view or multi-modal datasets. Especially, when certain samples miss an entire view of data, it creates the missing view problem.

DENOISING MATRIX COMPLETION