Turbo-SMT: Parallel Coupled Sparse Matrix-Tensor Factorizations and Applications.
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 enhanceanyCMTF 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 ofanyCMTF algorithm, produces sparse and interpretable solutions, and parallelizesanyCMTF algorithm, producing sparse and interpretable solutions (up to65 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.
Carnegie Mellon University.
Evangelos E
EE
Papalexakis
Carnegie Mellon University.
Christos
C
Faloutsos
Carnegie Mellon University.
Tom M
TM
Mitchell
Indian Institute of Science (IISc), Bangalore.
Partha Pratim
PP
Talukdar
University of Minnesota.
Nicholas D
ND
Sidiropoulos
Queen's University of Belfast.
Brian
B
Murphy
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