TY - JOUR
T1 - A novel relational regularization feature selection method for joint regression and classification in AD diagnosis
AU - Alzheimer's Disease Neuroimaging Initiative
AU - Zhu, Xiaofeng
AU - Suk, Heung Il
AU - Wang, Li
AU - Lee, Seong Whan
AU - Shen, Dinggang
N1 - Funding Information:
This work was supported in part by NIH grants (EB006733, EB008374, EB009634, MH100217, AG041721, AG042599), the ICT R&D program of MSIP/IITP [B0101-15-0307, Basic Software Research in Human-level Lifelong Machine Learning (Machine Learning Centre)], and the National Research Foundation of Korea (NRF) grant funded by the Korea government (NRF-2015R1A2A1A05001867). Xiaofeng Zhu was supported in part by the National Natural Science Foundation of China under grants (61263035 and 61573270), the Guangxi Natural Science Foundation under grant (2015GXNSFCB139011), and the funding of Guangxi 100 Plan.
Publisher Copyright:
© 2015 Elsevier B.V.
PY - 2017/5/1
Y1 - 2017/5/1
N2 - In this paper, we focus on joint regression and classification for Alzheimer's disease diagnosis and propose a new feature selection method by embedding the relational information inherent in the observations into a sparse multi-task learning framework. Specifically, the relational information includes three kinds of relationships (such as feature-feature relation, response–response relation, and sample-sample relation), for preserving three kinds of the similarity, such as for the features, the response variables, and the samples, respectively. To conduct feature selection, we first formulate the objective function by imposing these three relational characteristics along with an ℓ2,1-norm regularization term, and further propose a computationally efficient algorithm to optimize the proposed objective function. With the dimension-reduced data, we train two support vector regression models to predict the clinical scores of ADAS-Cog and MMSE, respectively, and also a support vector classification model to determine the clinical label. We conducted extensive experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset to validate the effectiveness of the proposed method. Our experimental results showed the efficacy of the proposed method in enhancing the performances of both clinical scores prediction and disease status identification, compared to the state-of-the-art methods.
AB - In this paper, we focus on joint regression and classification for Alzheimer's disease diagnosis and propose a new feature selection method by embedding the relational information inherent in the observations into a sparse multi-task learning framework. Specifically, the relational information includes three kinds of relationships (such as feature-feature relation, response–response relation, and sample-sample relation), for preserving three kinds of the similarity, such as for the features, the response variables, and the samples, respectively. To conduct feature selection, we first formulate the objective function by imposing these three relational characteristics along with an ℓ2,1-norm regularization term, and further propose a computationally efficient algorithm to optimize the proposed objective function. With the dimension-reduced data, we train two support vector regression models to predict the clinical scores of ADAS-Cog and MMSE, respectively, and also a support vector classification model to determine the clinical label. We conducted extensive experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset to validate the effectiveness of the proposed method. Our experimental results showed the efficacy of the proposed method in enhancing the performances of both clinical scores prediction and disease status identification, compared to the state-of-the-art methods.
KW - Alzheimer's disease
KW - Feature selection
KW - MCI conversion
KW - Manifold learning
KW - Sparse coding
UR - http://www.scopus.com/inward/record.url?scp=84949058046&partnerID=8YFLogxK
U2 - 10.1016/j.media.2015.10.008
DO - 10.1016/j.media.2015.10.008
M3 - Article
C2 - 26674971
AN - SCOPUS:84949058046
VL - 38
SP - 205
EP - 214
JO - Medical Image Analysis
JF - Medical Image Analysis
SN - 1361-8415
ER -