NDLI: Hierarchical tucker tensor regression: Application to brain imaging data analysis

Content Provider	IEEE Xplore Digital Library
Author	Ming Hou Chaib-draa, B.
Copyright Year	2015
Description	Author affiliation: Laval Univ., Quebec City, QC, Canada (Ming Hou; Chaib-draa, B.)
Abstract	We present a novel generalized linear tensor regression model, which takes tensor-variate inputs as covariates and finds low-rank (almost) best approximation of regression coefficient arrays using hierarchical Tucker decomposition. With limited sample size, our model is highly compact and extremely efficient as it requires only $O(dr^{3}$ + dpr) parameters for order d tensors of mode size p and rank r, which avoids the exponential growth in d, in contrast to $O(r^{d}$ + dpr) parameters of Tucker regression modeling. Our model also maintains the flexibility like classical Tucker regression by allowing distinct ranks on different modes according to a dimension tree structure. We evaluate our new model on both synthetic data and real-life MRI images to show its effectiveness.
Starting Page	1344
Ending Page	1348
File Size	638689
Page Count	5
File Format	PDF
e-ISBN	9781479983391
DOI	10.1109/ICIP.2015.7351019
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-09-27
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Tensile stress Brain modeling Data models Estimation Imaging Approximation methods Parameter estimation Magnetic Resonance Image Tensor Regression Hierarchical Tucker Decomposition Brain Imaging
Content Type	Text
Resource Type	Article

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