NDLI: Manifold Regularized Multitask Feature Learning for Multimodality Disease Classification

Content Provider	PubMed Central
Author	Jie, Biao Zhang, Daoqiang Cheng, Bo Shen, Dinggang
Copyright Year	2014
Abstract	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 modalities. However, one disadvantage of existing multimodality based methods is that they ignore the useful data distribution information in each modality, which is essential for subsequent classification. Accordingly, in this paper we propose a manifold regularized multitask feature learning method to preserve both the intrinsic relatedness among multiple modalities of data and the data distribution information in each modality. Specifically, we denote the feature learning on each modality as a single task, and use group-sparsity regularizer to capture the intrinsic relatedness among multiple tasks (i.e., modalities) and jointly select the common features from multiple tasks. Furthermore, we introduce a new manifold-based Laplacian regularizer to preserve the data distribution information from each task. Finally, we use the multikernel support vector machine method to fuse multimodality data for eventual classification. Conversely, we also extend our method to the semisupervised setting, where only partial data are labeled. We evaluate our method using the baseline magnetic resonance imaging (MRI), fluorodeoxyglucose positron emission tomography (FDG-PET), and cerebrospinal fluid (CSF) data of subjects from AD neuroimaging initiative database. The experimental results demonstrate that our proposed method can not only achieve improved classification performance, but also help to discover the disease-related brain regions useful for disease diagnosis.
Related Links	http://dx.doi.org/10.1002/hbm.22642
Ending Page	507
Page Count	19
Starting Page	489
File Format	PDF
ISSN	10659471
e-ISSN	10970193
Journal	Human brain mapping
Issue Number	2
Volume Number	36
Language	English
Publisher Date	2015-02-01
Access Restriction	Open
Subject Keyword	Anatomy Radiological and Ultrasound Technology Radiology Nuclear Medicine and imaging Neurology Clinical Neurology Research in Higher Education
Content Type	Text
Resource Type	Article
Subject	Anatomy Neurology Radiology, Nuclear Medicine and Imaging Neurology (clinical) Radiological and Ultrasound Technology

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