Advertisement

A Novel Multi-relation Regularization Method for Regression and Classification in AD Diagnosis

  • Xiaofeng Zhu
  • Heung-Il Suk
  • Dinggang Shen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8675)

Abstract

In this paper, we consider the joint regression and classification in Alzheimer’s disease diagnosis and propose a novel multi-relation regularization method that exploits the relational information inherent in the observations and then combines it with an ℓ2,1-norm within a least square regression framework for feature selection. Specifically, we use three kinds of relationships: feature-feature relation, response-response relation, and sample-sample relation. By imposing these three relational characteristics along with the ℓ2,1-norm on the weight coefficients, we formulate a new objective function. After feature selection based on the optimal weight coefficients, we train two support vector regression models to predict the clinical scores of Alzheimer’s Disease Assessment Scale-Cognitive subscale (ADAS-Cog) and Mini-Mental State Examination (MMSE), respectively, and a support vector classification model to identify the clinical label. We conducted clinical score prediction and disease status identification jointly on the Alzheimer’s Disease Neuroimaging Initiative dataset. The experimental results showed that the proposed regularization method outperforms the state-of-the-art methods, in the metrics of correlation coefficient and root mean squared error in regression and classification accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve in classification.

Keywords

Alzheimer’s disease feature selection sparse coding manifold learning MCI conversion 

References

  1. 1.
    Belkin, M., Niyogi, P., Sindhwani, V.: Manifold regularization: A geometric framework for learning from labeled and unlabeled examples. Journal of Machine Learning Research 7, 2399–2434 (2006)zbMATHMathSciNetGoogle Scholar
  2. 2.
    Chen, T., Kumar, R., Troianowski, G.A., Syeda-Mahmood, T.F., Beymer, D., Brannon, K.: Psar: Predictive space aggregated regression and its application in valvular heart disease classification. In: ISBI, pp. 1122–1125 (2013)Google Scholar
  3. 3.
    Cho, Y., Seong, J.K., Jeong, Y., Shin, S.Y.: Individual subject classification for Alzheimer’s disease based on incremental learning using a spatial frequency representation of cortical thickness data. NeuroImage 59(3), 2217–2230 (2012)CrossRefGoogle Scholar
  4. 4.
    Duchesne, S., Caroli, A., Geroldi, C., Collins, D.L., Frisoni, G.B.: Relating one-year cognitive change in mild cognitive impairment to baseline MRI features. NeuroImage 47(4), 1363–1370 (2009)CrossRefGoogle Scholar
  5. 5.
    Hastie, T., Tibshirani, R., Friedman, J., Franklin, J.: The elements of statistical learning: data mining, inference and prediction. The Mathematical Intelligencer 27(2), 83–85 (2005)Google Scholar
  6. 6.
    He, X., Cai, D., Niyogi, P.: Laplacian score for feature selection. In: NIPS, pp. 1–8 (2005)Google Scholar
  7. 7.
    Jie, B., Zhang, D., Cheng, B., Shen, D.: Manifold regularized multi-task feature selection for multi-modality classification in Alzheimers disease. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013, Part I. LNCS, vol. 8149, pp. 275–283. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  8. 8.
    Kabani, N.J.: 3D anatomical atlas of the human brain. NeuroImage 7, 0700–0717 (1998)Google Scholar
  9. 9.
    Liu, F., Suk, H.-I., Wee, C.-Y., Chen, H., Shen, D.: High-order graph matching based feature selection for Alzheimer’s disease identification. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013, Part II. LNCS, vol. 8150, pp. 311–318. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  10. 10.
    Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290, 2323–2326 (2000)CrossRefGoogle Scholar
  11. 11.
    Shen, D., Davatzikos, C.: HAMMER: hierarchical attribute matching mechanism for elastic registration. IEEE Transactions on Medical Imaging 21(11), 1421–1439 (2002)CrossRefGoogle Scholar
  12. 12.
    Suk, H.I., Lee, S.W.: A novel Bayesian framework for discriminative feature extraction in brain-computer interfaces. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(2), 286–299 (2013)CrossRefGoogle Scholar
  13. 13.
    Suk, H.-I., Shen, D.: Deep learning-based feature representation for AD/MCI classification. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013, Part II. LNCS, vol. 8150, pp. 583–590. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  14. 14.
    Wee, C.Y., Yap, P.T., Zhang, D., Denny, K., Browndyke, J.N., Potter, G.G., Welsh-Bohmer, K.A., Wang, L., Shen, D.: Identification of MCI individuals using structural and functional connectivity networks. Neuroimage 59(3), 2045–2056 (2012)CrossRefGoogle Scholar
  15. 15.
    Zhang, D., Shen, D.: Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer’s disease. NeuroImage 59(2), 895–907 (2012)CrossRefGoogle Scholar
  16. 16.
    Zhang, D., Wang, Y., Zhou, L., Yuan, H., Shen, D.: Multimodal classification of Alzheimer’s disease and mild cognitive impairment. NeuroImage 55(3), 856–867 (2011)CrossRefGoogle Scholar
  17. 17.
    Zhu, X., Huang, Z., Cheng, H., Cui, J., Shen, H.T.: Sparse hashing for fast multimedia search. ACM Transactions on Information Systems 31(2), 9 (2013)CrossRefGoogle Scholar
  18. 18.
    Zhu, X., Huang, Z., Cui, J., Shen, T.: Video-to-shot tag propagation by graph sparse group lasso. IEEE Transactions on Multimedia 13(3), 633–646 (2013)CrossRefGoogle Scholar
  19. 19.
    Zhu, X., Huang, Z., Shen, H.T., Cheng, J., Xu, C.: Dimensionality reduction by mixed kernel canonical correlation analysis. Pattern Recognition 45(8), 3003–3016 (2012)CrossRefzbMATHGoogle Scholar
  20. 20.
    Zhu, X., Huang, Z., Yang, Y., Shen, H.T., Xu, C., Luo, J.: Self-taught dimensionality reduction on the high-dimensional small-sized data. Pattern Recognition 46(1), 215–229 (2013)CrossRefzbMATHGoogle Scholar
  21. 21.
    Zhu, X., Suk, H.I., Shen, D.: Matrix-similarity based loss function and feature selection for Alzheimer’s disease diagnosis. In: CVPR (2014)Google Scholar
  22. 22.
    Zhu, X., Wu, X., Ding, W., Zhang, S.: Feature selection by joint graph sparse coding. In: SDM, pp. 803–811 (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Xiaofeng Zhu
    • 1
  • Heung-Il Suk
    • 1
  • Dinggang Shen
    • 1
  1. 1.Department of Radiology and Biomedical Research Imaging CenterUniversity of North Carolina at Chapel HillUSA

Personalised recommendations