In this paper, we propose a novel feature selection method by jointly considering (1) ‘task-specific’ relations between response variables (e.g., clinical labels in this work) and neuroimaging features and (2) ‘self-representation’ relations among neuroimaging features in a sparse regression framework. Specifically, the task-specific relation is devised to learn the relative importance of features for representation of response variables by a linear combination of the input features in a supervised manner, while the self-representation relation is used to take into account the inherent information among neuroimaging features such that any feature can be represented by a weighted sum of the other features, regardless of the label information, in an unsupervised manner. By integrating these two different relations along with a group sparsity constraint, we formulate a new sparse linear regression model for class-discriminative feature selection. The selected features are used to train a support vector machine for classification. To validate the effectiveness of the proposed method, we conducted experiments on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset; experimental results showed superiority of the proposed method over the state-of-the-art methods considered in this work.
Bibliographical noteFunding Information:
Acknowledgments This work was supported in part by NIH grants (EB006733, EB008374, MH100217, MH108914, AG041721, AG049371, AG042599, AG053867, EB022880, MH110274), the Nation Natural Science Foundation of China (Grant No: 61573270), the Brain Research Program through the National Research Foundation of Korea funded by the Ministry of Science, ICT & Future Planning (NRF-2014M3C7A1046050), the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning, and by the Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea government (No. 2017-0-00451).
© 2017, Springer Science+Business Media New York.
- Alzheimer’s disease (AD)
- Feature selection
- Joint sparse learning
- Mild cognitive impairment (MCI)
ASJC Scopus subject areas
- Radiology Nuclear Medicine and imaging
- Cognitive Neuroscience
- Clinical Neurology
- Cellular and Molecular Neuroscience
- Psychiatry and Mental health
- Behavioral Neuroscience