DinTucker: Scaling up Gaussian process models on multidimensional arrays with billions of elements

12 Nov 2013Shandian ZheYuan QiYoungja ParkIan MolloySuresh Chari

Infinite Tucker Decomposition (InfTucker) and random function prior models, as nonparametric Bayesian models on infinite exchangeable arrays, are more powerful models than widely-used multilinear factorization methods including Tucker and PARAFAC decomposition, (partly) due to their capability of modeling nonlinear relationships between array elements. Despite their great predictive performance and sound theoretical foundations, they cannot handle massive data due to a prohibitively high training time... (read more)

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