TY - JOUR
T1 - An efficient radius-incorporated MKL algorithm for Alzheimer's disease prediction
AU - Liu, Xinwang
AU - Zhou, Luping
AU - Wang, Lei
AU - Zhang, Jian
AU - Yin, Jianping
AU - Shen, Dinggang
N1 - Funding Information:
Dr. Xinwang Liu was supported by the National Basic Research Program of China (973) under Grant no. 2014CB340303 , the National Natural Science Foundation of China (project no. 61125201 , 61303189 , 61403405 and 61232016 ). Dr. Luping Zhou was supported by the UOW Vice Chancellor Research Grant. Dr. Dinggang Shen was supported in part by NIH grants EB006733 , EB008374 , EB009634 , AG041721 , MH100217 , and AG042599 .
Publisher Copyright:
© 2014 Elsevier Ltd.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2015/7/1
Y1 - 2015/7/1
N2 - Integrating multi-source information has recently shown promising performance in predicting Alzheimer's disease (AD). Multiple kernel learning (MKL) plays an important role in this regard by learning the combination weights of a set of base kernels via the principle of margin maximisation. The latest research on MKL further incorporates the radius of minimum enclosing ball (MEB) of training data to improve the kernel learning performance. However, we observe that directly applying these radius-incorporated MKL algorithms to AD prediction tasks does not necessarily improve, and sometimes even deteriorate, the prediction accuracy. In this paper, we propose an improved radius-incorporated MKL algorithm for AD prediction. First, we redesign the objective function by approximating the radius of MEB with its upper bound, a linear function of the kernel weights. This approximation makes the resulting optimisation problem convex and globally solvable. Second, instead of using cross-validation, we model the regularisation parameter C of the SVM classifier as an extra kernel weight and automatically tune it in MKL. Third, we theoretically show that our algorithm can be reformulated into a similar form of the SimpleMKL algorithm and conveniently solved by the off-the-shelf packages. We discuss the factors that contribute to the improved performance and apply our algorithm to discriminate different clinic groups from the benchmark ADNI data set. As experimentally demonstrated, our algorithm can better utilise the radius information and achieve higher prediction accuracy than the comparable MKL methods in the literature. In addition, our algorithm demonstrates the highest computational efficiency among all the comparable methods.
AB - Integrating multi-source information has recently shown promising performance in predicting Alzheimer's disease (AD). Multiple kernel learning (MKL) plays an important role in this regard by learning the combination weights of a set of base kernels via the principle of margin maximisation. The latest research on MKL further incorporates the radius of minimum enclosing ball (MEB) of training data to improve the kernel learning performance. However, we observe that directly applying these radius-incorporated MKL algorithms to AD prediction tasks does not necessarily improve, and sometimes even deteriorate, the prediction accuracy. In this paper, we propose an improved radius-incorporated MKL algorithm for AD prediction. First, we redesign the objective function by approximating the radius of MEB with its upper bound, a linear function of the kernel weights. This approximation makes the resulting optimisation problem convex and globally solvable. Second, instead of using cross-validation, we model the regularisation parameter C of the SVM classifier as an extra kernel weight and automatically tune it in MKL. Third, we theoretically show that our algorithm can be reformulated into a similar form of the SimpleMKL algorithm and conveniently solved by the off-the-shelf packages. We discuss the factors that contribute to the improved performance and apply our algorithm to discriminate different clinic groups from the benchmark ADNI data set. As experimentally demonstrated, our algorithm can better utilise the radius information and achieve higher prediction accuracy than the comparable MKL methods in the literature. In addition, our algorithm demonstrates the highest computational efficiency among all the comparable methods.
KW - Alzheimer's disease
KW - Multiple kernel learning
KW - Neuroimaging
KW - Radius-margin bound
KW - Support vector machines
UR - http://www.scopus.com/inward/record.url?scp=84925682671&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84925682671&partnerID=8YFLogxK
U2 - 10.1016/j.patcog.2014.12.007
DO - 10.1016/j.patcog.2014.12.007
M3 - Article
AN - SCOPUS:84925682671
VL - 48
SP - 2141
EP - 2150
JO - Pattern Recognition
JF - Pattern Recognition
SN - 0031-3203
IS - 7
ER -