Abstract
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.
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Notes
Available at ‘http://www.loni.usc.edu/ADNI’
Please refer to ‘www.adni-info.org’ for up-to-date information.
Refer to ‘http://www.adni-info.org’ for more details.
Available at ‘http://mipav.cit.nih.gov/clickwrap.php’
Note that we use a set of ROI volumes as features.
The term ‘self-similarity’, widely used in machine learning and computer vision, such as the literature (Liu et al. 2010; Zhu et al. 2015), indicates that each sample/feature can be represented by both other samples/features and itself. In this work, we assume that features are dependent, so it is reasonable to indicate that each ROI (or feature) can be sparsely represented by all ROIs (or features).
Available at ‘http://www.csie.ntu.edu.tw/cjlin/libsvm/’.
In Tables 2, 3, and 4, the boldface denotes the maximum performance in each column. (⋆ : Statistically significant from the proposed method with p < 0.05 and ∗: Statistically significant different from the proposed method with p < 0.001 on the paired-sample t-tests at 95% significance level between results of our method and all other competing methods).
The number in the parentheses represents an index of an ROI. Please refer to Table 7 for the full name of the respective ROIs.
Table 7 The names of the selected ROIs in this work
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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).
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Xiaofeng Zhu, Heung-Il Suk, Seong-Whan Lee, and Dinggang Shen declare that they have no conflict of interest.
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Zhu, X., Suk, HI., Lee, SW. et al. Discriminative self-representation sparse regression for neuroimaging-based alzheimer’s disease diagnosis. Brain Imaging and Behavior 13, 27–40 (2019). https://doi.org/10.1007/s11682-017-9731-x
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DOI: https://doi.org/10.1007/s11682-017-9731-x