title: Turbo-SMT: Accelerating Coupled Sparse Matrix-Tensor Factorizations by 200×.
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 theCoupled Matrix-Tensor Factorization(CMTF) problem. Can we accelerateanyCMTF 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 ofanyCMTF algorithm, by up to200×, along with an up to65 foldincrease in sparsity, with comparable accuracy to the baseline. 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.
authors.affiliation: Carnegie Mellon University.
authors.forename: Evangelos E
authors.initials: EE
authors.lastname: Papalexakis
authors.affiliation: Carnegie Mellon University.
authors.forename: Christos
authors.initials: C
authors.lastname: Faloutsos
authors.affiliation: Carnegie Mellon University.
authors.forename: Tom M
authors.initials: TM
authors.lastname: Mitchell
authors.affiliation: Carnegie Mellon University.
authors.forename: Partha Pratim
authors.initials: PP
authors.lastname: Talukdar
authors.affiliation: University of Minnesota.
authors.forename: Nicholas D
authors.initials: ND
authors.lastname: Sidiropoulos
authors.affiliation: Queen's University of Belfast.
authors.forename: Brian
authors.initials: B
authors.lastname: Murphy