Scalable Bayesian non-linear matrix completion

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussavertaisarvioitu

Tutkijat

Organisaatiot

  • Institute of Molecular Medicine Finland FIMM

Kuvaus

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.

Yksityiskohdat

AlkuperäiskieliEnglanti
OtsikkoProceedings of the 28th International Joint Conference on Artificial Intelligence, IJCAI 2019
ToimittajatSarit Kraus
TilaJulkaistu - 1 tammikuuta 2019
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaInternational Joint Conference on Artificial Intelligence - Venetian Macao Resort Hotel, Macao, Kiina
Kesto: 10 elokuuta 201916 elokuuta 2019
Konferenssinumero: 28
https://ijcai19.org/
http://ijcai19.org/

Julkaisusarja

NimiIJCAI International Joint Conference on Artificial Intelligence
KustantajaInternational Joint Conferences on Artificial Intelligence
Vuosikerta2019-August
ISSN (painettu)1045-0823

Conference

ConferenceInternational Joint Conference on Artificial Intelligence
LyhennettäIJCAI
MaaKiina
KaupunkiMacao
Ajanjakso10/08/201916/08/2019
www-osoite

ID: 38828046