Manifold Regularized Multi-Task Feature Selection for Multi-Modality Classification in Alzheimer’s Disease

  • Biao Jie
  • Daoqiang Zhang
  • Bo Cheng
  • Dinggang Shen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8149)


Accurate diagnosis of Alzheimer’s disease (AD), as well as its prodromal stage (i.e., mild cognitive impairment, MCI), is very important for possible delay and early treatment of the disease. Recently, multi-modality methods have been used for fusing information from multiple different and complementary imaging and non-imaging modalities. Although there are a number of existing multi-modality methods, few of them have addressed the problem of joint identification of disease-related brain regions from multi-modality data for classification. In this paper, we proposed a manifold regularized multi-task learning framework to jointly select features from multi-modality data. Specifically, we formulate the multi-modality classification as a multi-task learning framework, where each task focuses on the classification based on each modality. In order to capture the intrinsic relatedness among multiple tasks (i.e., modalities), we adopted a group sparsity regularizer, which ensures only a small number of features to be selected jointly. In addition, we introduced a new manifold based Laplacian regularization term to preserve the geometric distribution of original data from each task, which can lead to the selection of more discriminative features. Furthermore, we extend our method to the semi-supervised setting, which is very important since the acquisition of a large set of labeled data (i.e., diagnosis of disease) is usually expensive and time-consuming, while the collection of unlabeled data is relatively much easier. To validate our method, we have performed extensive evaluations on the baseline Magnetic resonance imaging (MRI) and fluorodeoxyglucose positron emission tomography (FDG-PET) data of Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Our experimental results demonstrate the effectiveness of the proposed method.


Mild Cognitive Impairment Unlabeled Data Geometric Distribution Unlabeled Sample Group Lasso 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Brookmeyer, R., Johnson, E., Ziegler-Graham, K., Arrighi, H.M.: Forecasting the global burden of Alzheimer’s disease. Alzheimers & Dementia 3, 186–191 (2007)CrossRefGoogle Scholar
  2. 2.
    Zhang, D., Wang, Y., Zhou, L., Yuan, H., Shen, D.: Multimodal classification of Alzheimer’s disease and mild cognitive impairment. Neuroimage 55, 856–867 (2011)CrossRefGoogle Scholar
  3. 3.
    Huang, S., Li, J., Ye, J., Chen, K., Wu, T.: Identifying Alzheimer’s Disease-Related Brain Regions from Multi-Modality Neuroimaging Data using Sparse Composite Linear Discrimination Analysis. In: Proceedings of Neural Information Processing Systems Conference (2011)Google Scholar
  4. 4.
    Cheng, B., Zhang, D., Shen, D.: Domain Transfer Learning for MCI Conversion Prediction. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part I. LNCS, vol. 7510, pp. 82–90. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  5. 5.
    Zhang, D., Shen, D.: Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer’s disease. Neuroimage 59, 895–907 (2012)CrossRefGoogle Scholar
  6. 6.
    Argyriou, A., Evgeniou, T., Pontil, M.: Multi-task Feature Learning. In: NIPS (2006)Google Scholar
  7. 7.
    Yuan, M., Lin, Y.: Model selection and estimation in regression with grouped variables. J. Roy. Stat. Soc. B 68, 49–67 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Zhu, X., Goldberg, A.B.: Introduction to semi-supervised learning. Morgan & Claypool, San Rafael (2009)zbMATHGoogle Scholar
  9. 9.
    Belkin, M., Niyogi, P., Sindhwani, V.: Manifold regularization: A geometric framework for learning from labeled and unlabeled examples. J. Mach. Learn. Res. 7, 2399–2434 (2006)MathSciNetzbMATHGoogle Scholar
  10. 10.
    Chen, X., Pan, W., Kwok, J.T., Carbonell, J.G.: Accelerated gradient method for multi-task sparse learning problem. In: ICDM (2009)Google Scholar
  11. 11.
    Liu, J., Ye, J.: Efficient L1/Lq Norm Regularization. Technical report, Arizona State University (2009)Google Scholar
  12. 12.
    Zhang, Y., Brady, M., Smith, S.: Segmentation of brain MR images through a hidden Markov random field model and the expectation maximization algorithm. IEEE Transactions on Medical Imaging 20, 45–57 (2001)CrossRefGoogle Scholar
  13. 13.
    Shen, D., Davatzikos, C.: HAMMER: Hierarchical attribute matching mechanism for elastic registration. IEEE Transactions on Medical Imaging 21, 1421–1439 (2002)CrossRefGoogle Scholar
  14. 14.
    Pudil, P., Novovičová, J., Kittler, J.: Floating search methods in feature selection. Pattern Recognition Letters 15, 1119–1125 (1994)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Biao Jie
    • 1
    • 2
  • Daoqiang Zhang
    • 1
  • Bo Cheng
    • 1
  • Dinggang Shen
    • 2
  1. 1.Dept. of Computer Science and EngineeringNanjing University of Aeronautics and AstronauticsNanjingChina
  2. 2.Dept. of Radiology and BRICUniversity of North Carolina at Chapel HillUSA

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