Manifold regularized multitask feature learning for multimodality disease classification

Multimodality based methods have shown great advantages in classification of Alzheimer's disease (AD) and its prodromal stage, that is, mild cognitive impairment (MCI). Recently, multitask feature selection methods are typically used for joint selection of common features across multiple modali...

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Bibliographic Details
Published in:	Human brain mapping Vol. 36; no. 2; pp. 489 - 507
Main Authors:	Jie, Biao, Zhang, Daoqiang, Cheng, Bo, Shen, Dinggang
Format:	Journal Article
Language:	English
Published:	United States Blackwell Publishing Ltd 01.02.2015 John Wiley & Sons, Inc John Wiley and Sons Inc
Subjects:	Aged Algorithms Alzheimer Disease - classification Alzheimer Disease - diagnosis Alzheimer Disease - physiopathology Alzheimer's disease Artificial Intelligence Brain - physiopathology Brain Mapping Cognitive Dysfunction - classification Cognitive Dysfunction - diagnosis Cognitive Dysfunction - physiopathology Databases, Factual feature selection Fluorodeoxyglucose F18 group-sparsity regularizer Humans Magnetic Resonance Imaging - methods manifold regularization Multimodal Imaging - methods multimodality classification multitask learning Pattern Recognition, Automated Positron-Emission Tomography - methods Radiopharmaceuticals manifold regularization group-sparsity regularizer multimodality classification multitask learning Alzheimer's disease feature selection
ISSN:	1065-9471, 1097-0193, 1097-0193
Online Access:	Get full text
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