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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| Vydané v: | Human brain mapping Ročník 36; číslo 2; s. 489 - 507 |
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| Hlavní autori: | , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
United States
Blackwell Publishing Ltd
01.02.2015
John Wiley & Sons, Inc John Wiley and Sons Inc |
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| ISSN: | 1065-9471, 1097-0193, 1097-0193 |
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| 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. Hum Brain Mapp 36:489–507, 2015. © 2014 Wiley Periodicals, Inc. |
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| AbstractList | 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. Hum Brain Mapp 36:489–507, 2015. © 2014 Wiley Periodicals, Inc. 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. Hum Brain Mapp 36:489-507, 2015. copyright 2014 Wiley Periodicals, Inc. 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. 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.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. 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. Hum Brain Mapp 36:489–507, 2015 . © 2014 Wiley Periodicals, Inc. |
| Author | Zhang, Daoqiang Shen, Dinggang Cheng, Bo Jie, Biao |
| AuthorAffiliation | 1 Department of Computer Science and Engineering Nanjing University of Aeronautics and Astronautics Nanjing China 2 Department of Computer Science and Technology Anhui Normal University Wuhu China 4 Department of Brain and Cognitive Engineering Korea University, Seoul Republic of Korea 3 Department of Radiology and BRIC University of North Carolina at Chapel Hill Chapel Hill North Carolina |
| AuthorAffiliation_xml | – name: 1 Department of Computer Science and Engineering Nanjing University of Aeronautics and Astronautics Nanjing China – name: 3 Department of Radiology and BRIC University of North Carolina at Chapel Hill Chapel Hill North Carolina – name: 2 Department of Computer Science and Technology Anhui Normal University Wuhu China – name: 4 Department of Brain and Cognitive Engineering Korea University, Seoul Republic of Korea |
| Author_xml | – sequence: 1 givenname: Biao surname: Jie fullname: Jie, Biao organization: Department of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China – sequence: 2 givenname: Daoqiang surname: Zhang fullname: Zhang, Daoqiang email: dqzhang@nuaa.edu.cn organization: Department of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China – sequence: 3 givenname: Bo surname: Cheng fullname: Cheng, Bo organization: Department of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China – sequence: 4 givenname: Dinggang surname: Shen fullname: Shen, Dinggang email: dqzhang@nuaa.edu.cn organization: Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/25277605$$D View this record in MEDLINE/PubMed |
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| ContentType | Journal Article |
| Copyright | 2014 Wiley Periodicals, Inc. 2015 Wiley Periodicals, Inc. |
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| CorporateAuthor | the Alzheimer's Disease Neuroimaging Initiative Alzheimer's Disease Neuroimaging Initiative |
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| DocumentTitleAlternate | Manifold Regularized Multitask Feature Learning |
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| ISSN | 1065-9471 1097-0193 |
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| Issue | 2 |
| Keywords | manifold regularization group-sparsity regularizer multimodality classification multitask learning Alzheimer's disease feature selection |
| Language | English |
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| Notes | istex:DE2C348E4671A776D1A8B4EFCF1ED5255616E230 the NUAA Fundamental Research Funds - No. NE2013105 National Institutes of Health Grant - No. U01 AG024904 NIH grants - No. EB006733; No. EB008374; No. EB009634; No. AG041721 ArticleID:HBM22642 ark:/67375/WNG-2JS5QJMN-F National Natural Science Foundation of China - No. 61422204; No. 61473149 the Jiangsu Natural Science Foundation for Distinguished Young Scholar - No. BK20130034 the Specialized Research Fund for the Doctoral Program of Higher Education - No. 20123218110009 the National Institute on Aging; the National Institute of Biomedical Imaging and Bioengineering; Abbott; AstraZeneca AB; Bayer Schering Pharma AG; Bristol-Myers Squibb; Eisai Global Clinical Development; Elan Corporation; Genentech; GE Healthcare; GlaxoSmithKline; Innogenetics; Johnson and Johnson; Eli Lilly and Co.; Medpace, Inc.; Merck and Co., Inc.; Novartis AG, Pfizer Inc; F. Hoffman-La Roche; Schering-Plough; Synarc, Inc.; as well as non-profit partners the Alzheimer's Association and Alzheimer's Drug Discovery Foundation, with participation from the U.S. Food and Drug Administration; Foundation for the National Institutes of Health (www.fnih.org) (ADNI) As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at www.loni.ucla.edu/ADNI/Collaboration/ADNI_Authorship_list.pdf Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database . www.loni.ucla.edu/ADNI ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Article-2 ObjectType-Undefined-1 ObjectType-Feature-3 content type line 23 Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (http://www.loni.ucla.edu/ADNI). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://www.loni.ucla.edu/ADNI/Collaboration/ADNI_Authorship_list.pdf. |
| OpenAccessLink | http://doi.org/10.1002/hbm.22642 |
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| PublicationDate | February 2015 |
| PublicationDateYYYYMMDD | 2015-02-01 |
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| PublicationPlace | United States |
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| PublicationTitle | Human brain mapping |
| PublicationTitleAlternate | Hum. Brain Mapp |
| PublicationYear | 2015 |
| Publisher | Blackwell Publishing Ltd John Wiley & Sons, Inc John Wiley and Sons Inc |
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| SubjectTerms | 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 |
| Title | Manifold regularized multitask feature learning for multimodality disease classification |
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