Tensors in Statistics

Xuan Bi, Xiwei Tang, Yubai Yuan, Yanqing Zhang, Annie Qu

Research output: Contribution to journalReview articlepeer-review

24 Scopus citations


This article provides an overview of tensors, their properties, and their applications in statistics. Tensors, also known as multidimensional arrays, are generalizations of matrices to higher orders and are useful data representation architectures. We first review basic tensor concepts and decompositions, and then we elaborate traditional and recent applications of tensors in the fields of recommender systems and imaging analysis. We also illustrate tensors for network data and explore the relations among interacting units in a complex network system. Some canonical tensor computational algorithms and available software libraries are provided for various tensor decompositions. Future research directions, including tensors in deep learning, are also discussed.

Original languageEnglish (US)
Pages (from-to)345-368
Number of pages24
JournalAnnual Review of Statistics and Its Application
StatePublished - Mar 7 2021

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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