CTT: Causally Informed Tensor Train Decomposition

Mao Lin Li, K. Selcuk Candan, Maria Luisa Sapino

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Tensor Train (TT) is a tensor decomposition technique designed to resolve the curse of dimensionality and the intermediate memory blow-up problems in traditional techniques for high-dimensional data analysis. Tensor train process provides linear space complexity by creating a sequential tensor network of low modalities. However, the selected sequence of decomposition order can have a significant impact on the accuracy and representativeness of the final decomposition and, unfortunately, choosing a good order for the TT representation is not a trivial task. In this paper, we observe that the causal structure underlying the data can impact the tensor train process and that a rough estimate of causality can be used to inform the order of the latent spaces to consider. Enlightened by this observation, we propose a novel causally informed tensor train decomposition (CTT) approach to tackle the sequence selection problem in TT-decomposition. CTT leverages the structural information in a given causal graph and recommends a suitable causally-informed decomposition sequence for TT-decomposition.

Original languageEnglish (US)
Title of host publicationProceedings - 2023 IEEE International Conference on Big Data, BigData 2023
EditorsJingrui He, Themis Palpanas, Xiaohua Hu, Alfredo Cuzzocrea, Dejing Dou, Dominik Slezak, Wei Wang, Aleksandra Gruca, Jerry Chun-Wei Lin, Rakesh Agrawal
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages8
ISBN (Electronic)9798350324457
StatePublished - 2023
Externally publishedYes
Event2023 IEEE International Conference on Big Data, BigData 2023 -
Duration: Jan 1 2023 → …

Publication series

NameProceedings - 2023 IEEE International Conference on Big Data, BigData 2023


Conference2023 IEEE International Conference on Big Data, BigData 2023
Period1/1/23 → …


  • Causality
  • Tensor
  • Tensor train decomposition

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Information Systems
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality


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