TY - GEN
T1 - Structural feature selection for connectivity network-based MCI diagnosis
AU - Jie, Biao
AU - Zhang, Daoqiang
AU - Wee, Chong Yaw
AU - Shen, Dinggang
PY - 2012
Y1 - 2012
N2 - Connectivity networks have been recently used for classification of neurodegenerative diseases, e.g., mild cognitive impairment (MCI). In typical connectivity network-based classification, features are often extracted from (multiple) connectivity networks and concatenated into a long vector for subsequent feature selection and classification. However, some useful network topological information may be lost in this type of approach. In this paper, we propose a new structural feature selection method which embeds the topological information of connectivity networks through graph kernel and then uses recursive feature elimination with graph kernel (RFE-GK) to select the most discriminative features. Furthermore, multiple kernel learning (MKL) is also adopted to combine multiple graph kernels for joint structural feature selectionfrom multiple connectivity networks. The experimental results show the efficacy of our proposed method with comparison to the state-of-the-art method in MCI classification, based on the connectivity networks.
AB - Connectivity networks have been recently used for classification of neurodegenerative diseases, e.g., mild cognitive impairment (MCI). In typical connectivity network-based classification, features are often extracted from (multiple) connectivity networks and concatenated into a long vector for subsequent feature selection and classification. However, some useful network topological information may be lost in this type of approach. In this paper, we propose a new structural feature selection method which embeds the topological information of connectivity networks through graph kernel and then uses recursive feature elimination with graph kernel (RFE-GK) to select the most discriminative features. Furthermore, multiple kernel learning (MKL) is also adopted to combine multiple graph kernels for joint structural feature selectionfrom multiple connectivity networks. The experimental results show the efficacy of our proposed method with comparison to the state-of-the-art method in MCI classification, based on the connectivity networks.
UR - http://www.scopus.com/inward/record.url?scp=84868262907&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84868262907&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-33530-3_15
DO - 10.1007/978-3-642-33530-3_15
M3 - Conference contribution
AN - SCOPUS:84868262907
SN - 9783642335297
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 175
EP - 184
BT - Multimodal Brain Image Analysis - Second International Workshop, MBIA 2012, Held in Conjunction with MICCAI 2012, Proceedings
T2 - 2nd International Workshop on Multimodal Brain Image Analysis, MBIA 2012, Held in Conjunction with the 15th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2012
Y2 - 1 October 2012 through 5 October 2012
ER -