TY - GEN
T1 - Integrating multiple network properties for MCI identification
AU - Jie, Biao
AU - Zhang, Daoqiang
AU - Suk, Heung Il
AU - Wee, Chong Yaw
AU - Shen, Dinggang
PY - 2013
Y1 - 2013
N2 - Recently, machine learning techniques have been actively applied to the identification of Alzheimer's disease (AD) and mild cognitive impairment (MCI). However, most of the existing methods focus on using only single network property, although combination of multiple network properties such as local connectivity and topological properties may be more powerful. Employing the kernel-based method, we propose a novel classification framework that attempts to integrate multiple network properties for improving the MCI classification. Specifically, two different types of kernel (i.e., vector-kernel and graph-kernel) extracted from multiple sub-networks are used to quantify two different yet complementary network properties. A multi-kernel learning technique is further adopted to fuse these heterogeneous kernels for MCI classification. Experimental results show that the proposed multiple-network- properties based method outperforms conventional single-network-property based methods.
AB - Recently, machine learning techniques have been actively applied to the identification of Alzheimer's disease (AD) and mild cognitive impairment (MCI). However, most of the existing methods focus on using only single network property, although combination of multiple network properties such as local connectivity and topological properties may be more powerful. Employing the kernel-based method, we propose a novel classification framework that attempts to integrate multiple network properties for improving the MCI classification. Specifically, two different types of kernel (i.e., vector-kernel and graph-kernel) extracted from multiple sub-networks are used to quantify two different yet complementary network properties. A multi-kernel learning technique is further adopted to fuse these heterogeneous kernels for MCI classification. Experimental results show that the proposed multiple-network- properties based method outperforms conventional single-network-property based methods.
UR - http://www.scopus.com/inward/record.url?scp=84886741411&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-02267-3_2
DO - 10.1007/978-3-319-02267-3_2
M3 - Conference contribution
AN - SCOPUS:84886741411
SN - 9783319022666
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 9
EP - 16
BT - Machine Learning in Medical Imaging - 4th International Workshop, MLMI 2013, Held in Conjunction with MICCAI 2013, Proceedings
PB - Springer Verlag
T2 - 4th International Workshop on Machine Learning in Medical Imaging, MLMI 2013, Held in Conjunction with 16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013
Y2 - 22 September 2013 through 22 September 2013
ER -