Tensor-Train Decomposition in Presence of Interval-Valued Data

Francesco Di Mauro, K. Selcuk Candan, Maria Luisa Sapino

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


In many fields of computer science, tensor decomposition techniques are increasingly being adopted as the core of many applications that rely on multi-dimensional datasets for implementing knowledge discovery tasks. Unfortunately, a major shortcoming of state-of-the-art tensor analyses is that, despite their effectiveness when the data is certain, these operations become difficult to apply, or altogether inapplicable, in presence of uncertainty in the data, a circumstance common to many real-world scenarios. In this paper we propose a way to address this issue by extending the known Tensor-Train technique for tensor factorization in order to deal with uncertain data, here modeled as intervals. Working with interval-valued data, however, presents numerous challenges, since many algebraic operations that form the building blocks of the factorization process, as well as the properties that make these procedures useful for knowledge discovery, cannot be easily extended from their scalar counterparts, and often require some approximation (including, though it is not only the case, for keeping computational costs manageable). These challenges notwithstanding, our proposed techniques proved to be reasonably effective, and are supported by a thorough experimental validation.

Original languageEnglish (US)
JournalIEEE Transactions on Knowledge and Data Engineering
StateAccepted/In press - 2021


  • Computer science
  • Computer vision
  • Data engineering
  • Interval-Valued Data
  • Knowledge discovery
  • Matrix decomposition
  • Task analysis
  • Tensor Factorization
  • Tensor-Train
  • Tensors
  • Uncertain Data

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics


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