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A selective overview of feature screening methods with applications to neuroimaging data
Author(s) -
He Kevin,
Xu Han,
Kang Jian
Publication year - 2018
Publication title -
wiley interdisciplinary reviews: computational statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.693
H-Index - 38
eISSN - 1939-0068
pISSN - 1939-5108
DOI - 10.1002/wics.1454
Subject(s) - neuroimaging , feature selection , computer science , artificial intelligence , dimensionality reduction , machine learning , feature (linguistics) , exploratory data analysis , voxel , pattern recognition (psychology) , functional magnetic resonance imaging , data mining , psychology , neuroscience , linguistics , philosophy
In neuroimaging studies, regression models are frequently used to identify the association of the imaging features and clinical outcome, where the number of imaging features (e.g., hundreds of thousands of voxel‐level predictors) much outweighs the number of subjects in the studies. Classical best subset selection or penalized variable selection methods that perform well for low‐ or moderate‐dimensional data do not scale to ultrahigh‐dimensional neuroimaging data. To reduce the dimensionality, variable screening has emerged as a powerful tool for feature selection in neuroimaging studies. We present a selective review of the recent developments in ultrahigh‐dimensional variable screening, with a focus on their practical performance on the analysis of neuroimaging data with complex spatial correlation structures and high‐dimensionality. We conduct extensive simulation studies to compare the performance on selection accuracy and computational costs between the different methods. We present analyses of resting‐state functional magnetic resonance imaging data in the Autism Brain Imaging Data Exchange study. This article is categorized under: Applications of Computational Statistics > Computational and Molecular Biology Statistical Learning and Exploratory Methods of the Data Sciences > Image Data Mining Statistical and Graphical Methods of Data Analysis > Analysis of High Dimensional Data

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