title: Randomized structural sparsity via constrained block subsampling for improved sensitivity of discriminative voxel identification.
abstract: In this paper, we consider voxel selection for functional Magnetic Resonance Imaging (fMRI) brain data with the aim of finding a more complete set of probably correlated discriminative voxels, thus improving interpretation of the discovered potential biomarkers. The main difficulty in doing this is an extremely high dimensional voxel space and few training samples, resulting in unreliable feature selection. In order to deal with the difficulty, stability selection has received a great deal of attention lately, especially due to its finite sample control of false discoveries and transparent principle for choosing a proper amount of regularization. However, it fails to make explicit use of the correlation property or structural information of these discriminative features and leads to large false negative rates. In other words, many relevant but probably correlated discriminative voxels are missed. Thus, we propose a new variant on stability selection "randomized structural sparsity", which incorporates the idea of structural sparsity. Numerical experiments demonstrate that our method can be superior in controlling for false negatives while also keeping the control of false positives inherited from stability selection.
authors.affiliation: School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731 PR China; Key laboratory for Neuroinformation of Ministry of Education, School of Life Science and Technology, Center for Information in Biomedicine, University of Electronic Science and Technology of China, Chengdu, Sichuan 611054, PR China; Center for Applied Mathematics, Cornell University, Ithaca, NY 14853, USA.
authors.forename: Yilun
authors.initials: Y
authors.lastname: Wang
authors.affiliation: Key laboratory for Neuroinformation of Ministry of Education, School of Life Science and Technology, Center for Information in Biomedicine, University of Electronic Science and Technology of China, Chengdu, Sichuan 611054, PR China.
authors.forename: Junjie
authors.initials: J
authors.lastname: Zheng
authors.affiliation: School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731 PR China.
authors.forename: Sheng
authors.initials: S
authors.lastname: Zhang
authors.affiliation: Key laboratory for Neuroinformation of Ministry of Education, School of Life Science and Technology, Center for Information in Biomedicine, University of Electronic Science and Technology of China, Chengdu, Sichuan 611054, PR China.
authors.forename: Xunjuan
authors.initials: X
authors.lastname: Duan
authors.affiliation: Key laboratory for Neuroinformation of Ministry of Education, School of Life Science and Technology, Center for Information in Biomedicine, University of Electronic Science and Technology of China, Chengdu, Sichuan 611054, PR China. Electronic address: chenhf@uestc.edu.cn.
authors.forename: Huafu
authors.initials: H
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D003198:Computer Simulation; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*
keywords: Constrained block subsampling; Feature selection; Pattern recognition; Randomized structural sparsity; Stability selection; Structural sparsity; Voxel selection; fMRI