Turbo-SMT: Accelerating coupled sparse matrix-tensor factorizations by 200x

Evangelos E. Papalexakis, Christos Faloutsos, Tom M. Mitchell, Partha Pratim Talukdar, Nicholas D. Sidiropoulos, Brian Murphy

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

33 Scopus citations


How can we correlate the neural activity in the human brain as it responds to typed words, with properties of these terms (like 'edible', 'fits in hand')? In short, we want to find latent variables, that jointly explain both the brain activity, as well as the behavioral responses. This is one of many settings of the Coupled Matrix-Tensor Factorization (CMTF) problem. Can we accelerate any CMTF solver, so that it runs within a few minutes instead of tens of hours to a day, while maintaining good accuracy? We introduce TURBO-SMT, a meta-method capable of doing exactly that: it boosts the performance of any CMTF algorithm, by up to 200x, along with an up to 65 fold increase in sparsity, with comparable accuracy to the baseline. We apply TURBO-SMT to BRAIN Q, a dataset consisting of a (nouns, brain voxels, human subjects) tensor and a (nouns, properties) matrix, with coupling along the nouns dimension. TURBO-SMT is able to find meaningful latent variables, as well as to predict brain activity with competitive accuracy.

Original languageEnglish (US)
Title of host publicationSIAM International Conference on Data Mining 2014, SDM 2014
EditorsMohammed J. Zaki, Arindam Banerjee, Srinivasan Parthasarathy, Pang Ning-Tan, Zoran Obradovic, Chandrika Kamath
PublisherSociety for Industrial and Applied Mathematics Publications
Number of pages9
ISBN (Electronic)9781510811515
StatePublished - 2014
Event14th SIAM International Conference on Data Mining, SDM 2014 - Philadelphia, United States
Duration: Apr 24 2014Apr 26 2014

Publication series

NameSIAM International Conference on Data Mining 2014, SDM 2014


Other14th SIAM International Conference on Data Mining, SDM 2014
Country/TerritoryUnited States

Bibliographical note

Funding Information:
Research was funded by grants NSF IIS-1247489, NSF IIS- 1247632, NSF CDI 0835797, NIH/NICHD 12165321, and DARPA FA87501320005. Any opinions, findings, and conclusions or recom­mendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the funding parties. The au­thors would also like to thank Leila Wehbe and Alona Fyshe for their initial help with the BrainQ data.

Publisher Copyright:
Copyright © SIAM.


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