CP tensor decomposition with cannot-link intermode constraints

Jette Henderson*, Bradley A. Malin, Joshua C. Denny, Abel N. Kho, Jimeng Sun, Joydeep Ghosh, Joyce C. Ho

*Corresponding author for this work

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

2 Scopus citations


Tensor factorization is a methodology that is applied in a variety of fields, ranging from climate modeling to medical informatics. A tensor is an n-way array that captures the relationship between n objects. These multiway arrays can be factored to study the underlying bases present in the data. Two challenges arising in tensor factorization are 1) the resulting factors can be noisy and highly overlapping with one another and 2) they may not map to insights within a domain. However, incorporating supervision to increase the number of insightful factors can be costly in terms of the time and domain expertise necessary for gathering labels or domain-specific constraints. To meet these challenges, we introduce CANDECOMP/PARAFAC (CP) tensor factorization with Cannot-Link Intermode Constraints (CP-CLIC), a framework that achieves succinct, diverse, interpretable factors. This is accomplished by gradually learning constraints that are verified with auxiliary information during the decomposition process. We demonstrate CP-CLIC’s potential to extract sparse, diverse, and interpretable factors through experiments on simulated data and a real-world application in medical informatics.

Original languageEnglish (US)
Title of host publicationSIAM International Conference on Data Mining, SDM 2019
PublisherSociety for Industrial and Applied Mathematics Publications
Number of pages9
ISBN (Electronic)9781611975673
StatePublished - 2019
Event19th SIAM International Conference on Data Mining, SDM 2019 - Calgary, Canada
Duration: May 2 2019May 4 2019

Publication series

NameSIAM International Conference on Data Mining, SDM 2019


Conference19th SIAM International Conference on Data Mining, SDM 2019

ASJC Scopus subject areas

  • Software


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