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 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 200×, along with an up to 65 fold increase 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.
Highlights
How is knowledge mapped and stored in the human brain? How is it expressed by people answering simple questions about specific words? If we have data from both worlds, are we able to combine them and jointly analyze them? In a very different scenario, suppose we have the social network graph of an online social network, and we have additional information about how and when users interacted with each other
What is a comprehensive way to combine those two pieces of data? Both, seemingly different, problems may be viewed as instances of what is called Coupled Matrix-Tensor Factorization (CMTF), where a data tensor and matrices that hold additional information are jointly decomposed into a set of low-rank factors
In the knowledge discovery part, the brain scan part of the dataset consists of fMRI scans first used in [11], a work that first demonstrated that brain activity can be predictably analyzed into component semantic features
Summary
How is knowledge mapped and stored in the human brain? How is it expressed by people answering simple questions about specific words? If we have data from both worlds, are we able to combine them and jointly analyze them? In a very different scenario, suppose we have the social network graph of an online social network, and we have additional information about how and when users interacted with each other. If we have data from both worlds, are we able to combine them and jointly analyze them? We introduce Turbo-SMT, a fast, scalable, and sparsity promoting CMTF meta-algorithm. Fast, parallel & triple-sparse algorithm: We provide an approximate, novel, scalable, and triplesparse (see Sec. 3) meta-method, Turbo-SMT, that is able to accelerate any CMTF core algorithm. Effectiveness & Knowledge Discovery: We analyze BrainQ, a brain scan dataset which is coupled to a semantic matrix (see Sec. 4 for details). In the knowledge discovery part, the brain scan part of the dataset consists of fMRI scans first used in [11], a work that first demonstrated that brain activity can be predictably analyzed into component semantic features. The Arxiv.org version has not been officially published in any conference proceedings or journal, and has evolved into this present work
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