Simple and efficient parallelization for probabilistic temporal tensor factorization
Abstract
Probabilistic Temporal Tensor Factorization (PTTF) is an effective algorithm to model the temporal tensor data. It leverages a time constraint to capture the evolving properties of tensor data. Nowadays the exploding dataset demands a large scale PTTF analysis, and a parallel solution is critical to accommodate the trend. Whereas, the parallelization of PTTF still remains unexplored. In this paper, we propose a simple yet efficient Parallel Probabilistic Temporal Tensor Factorization, referred to as P2T2F, to provide a scalable PTTF solution. P2T2F is fundamentally disparate from existing parallel tensor factorizations by considering the probabilistic decomposition and the temporal effects of tensor data. It adopts a new tensor data split strategy to subdivide a large tensor into independent sub-tensors, the computation of which is inherently parallel. We train P2T2F with an efficient algorithm of stochastic Alternating Direction Method of Multipliers, and show that the convergence is guaranteed. Experiments on several real-word tensor datasets demonstrate that P2T2F is a highly effective and efficiently scalable algorithm dedicated for large scale probabilistic temporal tensor analysis.
Authors: Guangxi Li, Zenglin Xu, Linnan Wang, Jinmian Ye, Irwin King, Michael Rung-Tsong Lyu