Experiments demonstrate that DINTUCKER maintains the predictive accuracy of InfTucker and is scalable on massive data: On multidimensional arrays with billions.
Mar 2, 2016 · Tensor decomposition methods are effective tools for modelling multidimensional array data (i.e., tensors). Among them, nonparametric Bayesian ...
Nov 12, 2013 · DINTUCKER is based on a new hierarchical Bayesian model that enables local training of InfTucker on subarrays and information integration from ...
DinTucker is proposed, a new hierarchical Bayesian model that enables local learning of InfTucker on subarrays and global information integration from local ...
(PDF) DinTucker: Scaling up Gaussian process models on ...
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We apply DinTucker to multidimensional arrays with billions of elements from applications in the "Read the Web" project (Carlson et al. 2010) and in information ...
Experiments demonstrate that DinTucker maintains the predictive accuracy of InfTucker and is scalable on massive data: On multidimensional arrays with billions ...
Feb 1, 2014 · In this paper, we propose Distributed infinite Tucker (DINTUCKER), a large-scale nonlinear tensor decomposition algorithm on MAPREDUCE. It keeps ...
Tensor decomposition methods are effective tools for modelling multidimensional array data (i.e., tensors). Among them, nonparametric Bayesian models, ...
Tensor decomposition methods are effective tools for modelling multidimensional array data (i.e., tensors). Among them, nonparametric Bayesian models, ...
Dintucker: Scaling up gaussian process models on large multidimensional arrays. S Zhe, Y Qi, Y Park, Z Xu, I Molloy, S Chari. Proceedings of the AAAI Conference ...