Dimensionality reduction and topographic mapping of binary tensors

Jakub Mažgut*, Peter Tino, Mikael Bodén, Hong Yan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


In this paper, a decomposition method for binary tensors, generalized multi-linear model for principal component analysis (GMLPCA) is proposed. To the best of our knowledge at present there is no other principled systematic framework for decomposition or topographic mapping of binary tensors. In the model formulation, we constrain the natural parameters of the Bernoulli distributions for each tensor element to lie in a sub-space spanned by a reduced set of basis (principal) tensors. We evaluate and compare the proposed GMLPCA technique with existing real-valued tensor decomposition methods in two scenarios: (1) in a series of controlled experiments involving synthetic data; (2) on a real-world biological dataset of DNA sub-sequences from different functional regions, with sequences represented by binary tensors. The experiments suggest that the GMLPCA model is better suited for modelling binary tensors than its real-valued counterparts. Furthermore, we extended our GMLPCA model to the semi-supervised setting by forcing the model to search for a natural parameter subspace that represents a user-specified compromise between the modelling quality and the degree of class separation.

Original languageEnglish
Pages (from-to)497-515
Number of pages19
JournalPattern Analysis and Applications
Issue number3
Early online date1 Feb 2013
Publication statusPublished - Aug 2014


  • Binary data
  • Tensor decomposition
  • Topographic mapping
  • Tucker model

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition


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