Gtt: Guiding the tensor train decomposition

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

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

2 Scopus citations


The demand for searching, querying multimedia data such as image, video and audio is omnipresent, how to effectively access data for various applications is a critical task. Nevertheless, these data usually are encoded as multi-dimensional arrays, or Tensor, and traditional data mining techniques might be limited due to the curse of dimensionality. Tensor decomposition is proposed to alleviate this issue, commonly used tensor decomposition algorithms include CP-decomposition (which seeks a diagonal core) and Tucker-decomposition (which seeks a dense core). Naturally, Tucker maintains more information, but due to the denseness of the core, it also is subject to exponential memory growth with the number of tensor modes. Tensor train (TT) decomposition addresses this problem by seeking a sequence of three-mode cores: but unfortunately, currently, there are no guidelines to select the decomposition sequence. In this paper, we propose a GTT method for guiding the tensor train in selecting the decomposition sequence. GTT leverages the data characteristics (including number of modes, length of the individual modes, density, distribution of mutual information, and distribution of entropy) as well as the target decomposition rank to pick a decomposition order that will preserve information. Experiments with various data sets demonstrate that GTT effectively guides the TT-decomposition process towards decomposition sequences that better preserve accuracy.

Original languageEnglish (US)
Title of host publicationSimilarity Search and Applications - 13th International Conference, SISAP 2020, Proceedings
EditorsShin’ichi Satoh, Lucia Vadicamo, Fabio Carrara, Arthur Zimek, Ilaria Bartolini, Martin Aumüller, Bjorn Por Jonsson, Rasmus Pagh
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages16
ISBN (Print)9783030609351
StatePublished - 2020
Event13th International Conference on Similarity Search and Applications, SISAP 2020 - Copenhagen, Denmark
Duration: Sep 30 2020Oct 2 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12440 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference13th International Conference on Similarity Search and Applications, SISAP 2020


  • Low-rank embedding
  • Tensor train decomposition

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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