Scalable Bayesian non-linear matrix completion

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Researchers

Research units

  • Institute of Molecular Medicine Finland FIMM

Abstract

Matrix completion aims to predict missing elements in a partially observed data matrix which in typical applications, such as collaborative filtering, is large and extremely sparsely observed. A standard solution is matrix factorization, which predicts unobserved entries as linear combinations of latent variables. We generalize to non-linear combinations in massive-scale matrices. Bayesian approaches have been proven beneficial in linear matrix completion, but not applied in the more general non-linear case, due to limited scalability. We introduce a Bayesian non-linear matrix completion algorithm, which is based on a recent Bayesian formulation of Gaussian process latent variable models. To solve the challenges regarding scalability and computation, we propose a data-parallel distributed computational approach with a restricted communication scheme. We evaluate our method on challenging out-of-matrix prediction tasks using both simulated and real-world data.

Details

Original languageEnglish
Title of host publicationProceedings of the 28th International Joint Conference on Artificial Intelligence, IJCAI 2019
EditorsSarit Kraus
Publication statusPublished - 1 Jan 2019
MoE publication typeA4 Article in a conference publication
EventInternational Joint Conference on Artificial Intelligence - Venetian Macao Resort Hotel, Macao, China
Duration: 10 Aug 201916 Aug 2019
Conference number: 28
https://ijcai19.org/
http://ijcai19.org/

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
PublisherInternational Joint Conferences on Artificial Intelligence
Volume2019-August
ISSN (Print)1045-0823

Conference

ConferenceInternational Joint Conference on Artificial Intelligence
Abbreviated titleIJCAI
CountryChina
CityMacao
Period10/08/201916/08/2019
Internet address

ID: 38828046