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Title: Learning Multiple Networks via Supervised Tensor Decomposition
We consider the problem of tensor decomposition with multiple side information available as interactive features. Such problems are common in neuroimaging, network modeling, and spatial-temporal analysis. We develop a new family of exponential tensor decomposition models and establish the theoretical accuracy guarantees. An efficient alternating optimization algorithm is further developed. Unlike earlier methods, our proposal is able to handle a broad range of data types, including continuous, count, and binary observations. We apply the method to diffusion tensor imaging data from human connectome project and identify the key brain connectivity patterns associated with available features. Our method will help the practitioners efficiently analyze tensor datasets in various areas. Toward this end, all data and code are available at https://CRAN.R-project.org/ package=tensorregress.  more » « less
Award ID(s):
1915978
NSF-PAR ID:
10345832
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Third Workshop on Machine Learning and the Physical Sciences (NeurIPS 2020), Vancouver, Canada.
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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