title: Multi-view Classification for Identification of Alzheimer's Disease.
abstract: In this paper, we propose a multi-view learning method using Magnetic Resonance Imaging (MRI) data for Alzheimer's Disease (AD) diagnosis. Specifically, we extract both Region-Of-Interest (ROI) features and Histograms of Oriented Gradient (HOG) features from each MRI image, and then propose mapping HOG features onto the space of ROI features to make them comparable and to impose high intra-class similarity with low inter-class similarity. Finally, both mapped HOG features and original ROI features are input to the support vector machine for AD diagnosis. The purpose of mapping HOG features onto the space of ROI features is to provide complementary information so that features from different views cannot onlybe comparable (i.e.,homogeneous)but alsobe interpretable. For example, ROI features are robust to noise, but lack of reflecting small or subtle changes, while HOG features are diverse but less robust to noise. The proposed multi-view learning method is designed to learn the transformation between two spaces and to separate the classes under the supervision of class labels. The experimental results on the MRI images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset show that the proposed multi-view method helps enhance disease status identification performance, outperforming both baseline methods and state-of-the-art methods.
authors.affiliation: Department of Radiology and BRIC, University of North Carolina, Chapel Hill, USA.
authors.forename: Xiaofeng
authors.initials: X
authors.lastname: Zhu
authors.affiliation: Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea.
authors.forename: Heung-Il
authors.initials: HI
authors.lastname: Suk
authors.affiliation: School of Computer, Electronics and Information, Guangxi University, Nanning, China.
authors.forename: Yonghua
authors.initials: Y
authors.lastname: Zhu
authors.affiliation: Department of Radiology and BRIC, University of North Carolina, Chapel Hill, USA.
authors.forename: Kim-Han
authors.initials: KH
authors.lastname: Thung
authors.affiliation: Department of Radiology and BRIC, University of North Carolina, Chapel Hill, USA.
authors.forename: Guorong
authors.initials: G
authors.lastname: Wu
authors.affiliation: Department of Radiology and BRIC, University of North Carolina, Chapel Hill, USA.
authors.forename: Dinggang
authors.initials: D
authors.lastname: Shen