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

Papalexakis, Evangelos E and Mitchell, Tom M and Sidiropoulos, Nicholas D and Faloutsos, Christos and Talukdar, Partha Pratim and Murphy, Brian (2016) Turbo-SMT: Parallel coupled sparse matrix-Tensor factorizations and applications. In: STATISTICAL ANALYSIS AND DATA MINING, 9 (4). pp. 269-290.

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Official URL: http://dx.doi.org/10.1002/sam.11315

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. (c) 2016 Wiley Periodicals, Inc. Statistical Analysis and Data Mining: The ASA Data Science Journal, 2016

Item Type: Journal Article
Additional Information: Copy right for this article belongs to the WILEY-BLACKWELL, 111 RIVER ST, HOBOKEN 07030-5774, NJ USA
Keywords: algorithm;coupled matrix-tensor factorization;fMRI data;neurosemantics;parallel;sparse;speedup;tensor
Department/Centre: Division of Electrical Sciences > Computer Science & Automation
Depositing User: Id for Latest eprints
Date Deposited: 23 Aug 2016 10:42
Last Modified: 09 Mar 2019 13:08
URI: http://eprints.iisc.ac.in/id/eprint/54454

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