Tensor-Train-Based High-Order Dominant Eigen Decomposition for Multimodal Prediction Services

Huazhong Liu, Laurence Tianruo Yang, Jihong Ding, Yimu Guo, Stephen S. Yau

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

By leveraging neoteric analytical techniques associated with big data, numerous new data-focused computation and service models have flourished in service computing systems. Accurate future predictions based on tensor-based multivariate Markov models can vigorously support enterprise decisions. However, the computation efficiency and quick response of tensor-based multimodal prediction approach are seriously restricted by the curse of dimensionality arising from high-order tensor. Therefore, to alleviate the problem, this paper focuses on proposing a tensor-train (TT)-based computation approach with its scalable implementation for high-order dominant eigen decomposition (HODED) in multivariate Markov models. First, we present a TT-based Einstein product directly based on decomposed TT cores and guarantee that the result remains TT format. Then, we put forward a scalable implementation for TT-based Einstein product in a distributed or parallel manner. Afterwards, we propose a scalable TT-based HODED (TT-HODED) algorithm and a multimodal accurate prediction algorithm. Furthermore, a TT-based big data processing and services framework is presented to provide accurate proactive services. Experimental results based on real-world GPS trajectory dataset demonstrate that TT-HODED algorithm can significantly improve the computation efficiency and reduce the running memory on the premise of guaranteeing the almost consistent prediction accuracy compared to the original HODED algorithm.

Original languageEnglish (US)
Article number8753660
Pages (from-to)197-211
Number of pages15
JournalIEEE Transactions on Engineering Management
Volume68
Issue number1
DOIs
StatePublished - Feb 2021

Keywords

  • Accurate services
  • big data
  • high-order dominant eigen decomposition (HODED)
  • multimodal prediction
  • multivariate Markov model
  • scalable tensor computations
  • tensor-train (TT)-based Einstein product

ASJC Scopus subject areas

  • Strategy and Management
  • Electrical and Electronic Engineering

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