Simple and Efficient Parallelization for Probabilistic Temporal Tensor Factorization

TL;DR

This paper introduces P^2T^2F, a scalable parallel algorithm for probabilistic temporal tensor factorization that effectively handles large datasets by leveraging a new data split strategy and stochastic ADMM, ensuring convergence.

Contribution

It proposes a novel parallelization method for PTTF that considers probabilistic and temporal aspects, with a new tensor split strategy and guaranteed convergence.

Findings

Demonstrates high efficiency and scalability on real-world datasets.

Achieves effective large-scale probabilistic temporal tensor analysis.

Outperforms existing methods in speed and accuracy.

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 P$^2$T$^2$F, to provide a scalable PTTF solution. P$^2$T$^2$F 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 P$^2$T$^2$F 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 P$^2$T$^2$F is a highly effective and efficiently scalable algorithm dedicated for large scale probabilistic temporal tensor analysis.