TY - GEN
T1 - Inter-modality relationship constrained multi-task feature selection for AD/MCI classification
AU - Liu, Feng
AU - Wee, Chong Yaw
AU - Chen, Huafu
AU - Shen, Dinggang
PY - 2013
Y1 - 2013
N2 - In conventional multi-modality based classification framework, feature selection is typically performed separately for each individual modality, ignoring potential strong inter-modality relationship of the same subject. To extract this inter-modality relationship, L2,1 norm-based multi-task learning approach can be used to jointly select common features from different modalities. Unfortunately, this approach overlooks different yet complementary information conveyed by different modalities. To address this issue, we propose a novel multi-task feature selection method to effectively preserve the complementary information between different modalities, improving brain disease classification accuracy. Specifically, a new constraint is introduced to preserve the inter-modality relationship by treating the feature selection procedure of each modality as a task. This constraint preserves distance between feature vectors from different modalities after projection to low dimensional feature space. We evaluated our method on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and obtained significant improvement on Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI) classification compared to state-of-the-art methods.
AB - In conventional multi-modality based classification framework, feature selection is typically performed separately for each individual modality, ignoring potential strong inter-modality relationship of the same subject. To extract this inter-modality relationship, L2,1 norm-based multi-task learning approach can be used to jointly select common features from different modalities. Unfortunately, this approach overlooks different yet complementary information conveyed by different modalities. To address this issue, we propose a novel multi-task feature selection method to effectively preserve the complementary information between different modalities, improving brain disease classification accuracy. Specifically, a new constraint is introduced to preserve the inter-modality relationship by treating the feature selection procedure of each modality as a task. This constraint preserves distance between feature vectors from different modalities after projection to low dimensional feature space. We evaluated our method on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and obtained significant improvement on Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI) classification compared to state-of-the-art methods.
KW - Alzheimer's Disease
KW - Multi-kernel support vector machine
KW - Multi-modality
KW - Multi-task learning
KW - Sparse representation
UR - http://www.scopus.com/inward/record.url?scp=84894638738&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-40811-3_39
DO - 10.1007/978-3-642-40811-3_39
M3 - Conference contribution
C2 - 24505680
AN - SCOPUS:84894638738
SN - 9783642408106
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 308
EP - 315
BT - Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings
T2 - 16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013
Y2 - 22 September 2013 through 26 September 2013
ER -