Math @ Duke

Publications [#342197] of David B. Dunson
search arxiv.org.Papers Published
 Wang, L; Zhang, Z; Dunson, D, Symmetric Bilinear Regression for Signal Subgraph Estimation,
Ieee Transactions on Signal Processing, vol. 67 no. 7
(April, 2019),
pp. 19291940 [doi]
(last updated on 2019/05/22)
Abstract: © 19912012 IEEE. There is an increasing interest in learning a set of small outcomerelevant subgraphs in networkpredictor regression. The extracted signal subgraphs can greatly improve the interpretation of the association between the network predictor and the response. In brain connectomics, the brain network for an individual corresponds to a set of interconnections among brain regions and there is a strong interest in linking the brain connectome to human cognitive traits. Modern neuroimaging technology allows a very fine segmentation of the brain, producing very large structural brain networks. Therefore, accurate and efficient methods for identifying a set of small predictive subgraphs become crucial, leading to discovery of key interconnected brain regions related to the trait and important insights on the mechanism of variation in human cognitive traits. We propose a symmetric bilinear model with $L1$ penalty to search for small clique subgraphs that contain useful information about the response. A coordinate descent algorithm is developed to estimate the model where we derive analytical solutions for a sequence of conditional convex optimizations. Application of this method on human connectome and language comprehension data shows interesting discovery of relevant interconnections among several small sets of brain regions and better predictive performance than competitors.


dept@math.duke.edu
ph: 919.660.2800
fax: 919.660.2821
 
Mathematics Department
Duke University, Box 90320
Durham, NC 277080320

