Add to ORKGBayesian matrix completion has been studied based on a low-rank matrix factorization formulation with promising results. However, little work has been done on Bayesian matrix completion based on the more direct spectral regularization formulation. We fill this gap by presenting a novel Bayesian matrix completion method based on spectral regularization. In order to circumvent the difficulties of dealing with the orthonormality constraints of singular vectors, we derive a new equivalent form with relaxed constraints, which then leads us to design an adaptive version of spectral regularization feasible for Bayesian inference. Our Bayesian method requires no parameter tuning and can infer the number of latent factors automatically. Experiments ...
Low-rank matrix estimation from incomplete measurements recently received increased attention due t...
Matrix factorization is a fundamental technique in machine learning that is applicable to collaborat...
This paper studies the matrix completion problem under arbitrary sampling schemes. We propose a new ...
We propose a novel class of algorithms for low rank matrix completion. Our approach builds on novel ...
We propose a novel class of algorithms for low rank matrix completion. Our ap-proach builds on novel...
Recovery of low-rank matrices has recently seen significant activity in many areas of science and en...
In the fields of artificial intelligence and machine learning, we are often interested in making pro...
In the paper we propose a new type of regularization procedure for training sparse Bayesian methods ...
Recently, there is a revival of interest in low-rank matrix completion-based unsupervised learning t...
Extracting low-rank and/or sparse structures using matrix factorization techniques has been extensiv...
Extracting low-rank and/or sparse structures using matrix factorization techniques has been extensiv...
Bayesian methods for low-rank matrix completion with noise have been shown to be very efficient comp...
Bayesian methods for low-rank matrix completion with noise have been shown to be very efficient comp...
We discuss how a large class of regularization methods, collectively known as spectral regularizatio...
Low-rank matrix estimation from incomplete measurements recently received increased attention due to...
Low-rank matrix estimation from incomplete measurements recently received increased attention due t...
Matrix factorization is a fundamental technique in machine learning that is applicable to collaborat...
This paper studies the matrix completion problem under arbitrary sampling schemes. We propose a new ...
We propose a novel class of algorithms for low rank matrix completion. Our approach builds on novel ...
We propose a novel class of algorithms for low rank matrix completion. Our ap-proach builds on novel...
Recovery of low-rank matrices has recently seen significant activity in many areas of science and en...
In the fields of artificial intelligence and machine learning, we are often interested in making pro...
In the paper we propose a new type of regularization procedure for training sparse Bayesian methods ...
Recently, there is a revival of interest in low-rank matrix completion-based unsupervised learning t...
Extracting low-rank and/or sparse structures using matrix factorization techniques has been extensiv...
Extracting low-rank and/or sparse structures using matrix factorization techniques has been extensiv...
Bayesian methods for low-rank matrix completion with noise have been shown to be very efficient comp...
Bayesian methods for low-rank matrix completion with noise have been shown to be very efficient comp...
We discuss how a large class of regularization methods, collectively known as spectral regularizatio...
Low-rank matrix estimation from incomplete measurements recently received increased attention due to...
Low-rank matrix estimation from incomplete measurements recently received increased attention due t...
Matrix factorization is a fundamental technique in machine learning that is applicable to collaborat...
This paper studies the matrix completion problem under arbitrary sampling schemes. We propose a new ...