Turbo-SMT: Parallel coupled sparse matrix-Tensor factorizations and applications

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

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

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 enhance any CMTF solver, so that it can operate on potentially very large datasets that may not fit in main memory? We introduce Turbo-SMT, a meta-method capable of doing exactly that: it boosts the performance of any CMTF algorithm, produces sparse and interpretable solutions, and parallelizes any CMTF algorithm, producing sparse and interpretable solutions (up to 65 fold). Additionally, we improve upon ALS, the work-horse algorithm for CMTF, with respect to efficiency and robustness to missing values. We apply Turbo-SMT to BrainQ, 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. Finally, we demonstrate the generality of Turbo-SMT, by applying it on a FACEBOOK dataset (users, ‘friends', wall-postings); there, Turbo-SMT spots spammer-like anomalies.

Original languageEnglish (US)
Pages (from-to)269-290
Number of pages22
JournalStatistical Analysis and Data Mining
Volume9
Issue number4
DOIs
StatePublished - Aug 1 2016

Keywords

  • algorithm
  • coupled matrix-tensor factorization
  • fMRI data
  • neurosemantics
  • parallel
  • sparse
  • speedup
  • tensor

Fingerprint Dive into the research topics of 'Turbo-SMT: Parallel coupled sparse matrix-Tensor factorizations and applications'. Together they form a unique fingerprint.

Cite this