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arXiv · 1908.01009

Scalable Bayesian Non-linear Matrix Completion

Xiangju Qin·Paul Blomstedt·Samuel Kaski
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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.

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Xiangju Qin, Paul Blomstedt, Samuel Kaski. 2019-07-31. Scalable Bayesian Non-linear Matrix Completion. https://arxiv.org/abs/1908.01009

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