Coupled graph tensor factorization

Ahmed S. Zamzam, Vassilis N. Ioannidis, Nicholas D. Sidiropoulos

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

5 Scopus citations


Factorization of a single matrix or tensor has been used widely to reveal interpretable factors or predict missing data. However, in many cases side information may be available, such as social network activities and user demographic data together with Netflix data. In these situations, coupled matrix tensor factorization (CMTF) can be employed to account for additional sources of information. When the side information comes in the form of item-correlation matrices of certain modes, existing CMTF algorithms do not apply. Instead, a novel approach to model the correlation matrices is proposed here, using symmetric nonnegative matrix factorization. The multiple sources of information are fused by fitting outer-product models for the tensor and the correlation matrices in a coupled manner. The proposed model has the potential to overcome practical challenges, such as missing slabs from the tensor and/or missing rows/columns from the correlation matrices.

Original languageEnglish (US)
Title of host publicationConference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Number of pages5
ISBN (Electronic)9781538639542
StatePublished - Mar 1 2017
Event50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016 - Pacific Grove, United States
Duration: Nov 6 2016Nov 9 2016

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)1058-6393


Other50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016
Country/TerritoryUnited States
CityPacific Grove

Bibliographical note

Funding Information:
A.S. Zamzam and N.D. Sidiropoulos were partially supported by NSF CIF-1525194, ECCS-1231504

Publisher Copyright:
© 2016 IEEE.


  • Tensor factorization
  • matrix factorization
  • missing entries
  • parafac model


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