TY - JOUR
AU1 - Zhu, Xiaofeng
AU2 - Suk, Heung-Il
AU3 - Lee, Seong-Whan
AU4 - Shen, Dinggang
AB - 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.
TI - Discriminative self-representation sparse regression for neuroimaging-based alzheimer’s disease diagnosis
JF - Brain Imaging and Behavior
DO - 10.1007/s11682-017-9731-x
DA - 2017-06-17
UR - https://www.deepdyve.com/lp/springer-journals/discriminative-self-representation-sparse-regression-for-neuroimaging-JrSSK67Cu8
SP - 27
EP - 40
VL - 13
IS - 1
DP - DeepDyve
ER -