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.
Original language | English |
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Title of host publication | Proceedings of the 28th International Joint Conference on Artificial Intelligence, IJCAI 2019 |
Editors | Sarit Kraus |
Pages | 3275-3281 |
Number of pages | 7 |
ISBN (Electronic) | 9780999241141 |
Publication status | Published - 1 Jan 2019 |
MoE publication type | A4 Article in a conference publication |
Event | International Joint Conference on Artificial Intelligence - Venetian Macao Resort Hotel, Macao, China Duration: 10 Aug 2019 → 16 Aug 2019 Conference number: 28 https://ijcai19.org/ http://ijcai19.org/ |
Publication series
Name | IJCAI International Joint Conference on Artificial Intelligence |
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Publisher | International Joint Conferences on Artificial Intelligence |
Volume | 2019-August |
ISSN (Print) | 1045-0823 |
Conference
Conference | International Joint Conference on Artificial Intelligence |
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Abbreviated title | IJCAI |
Country | China |
City | Macao |
Period | 10/08/2019 → 16/08/2019 |
Internet address |