Sparse Multi-kernel Based Multi-task Learning for Joint Prediction of Clinical Scores and Biomarker Identification in Alzheimer’s Disease

  • Peng CaoEmail author
  • Xiaoli Liu
  • Jinzhu Yang
  • Dazhe Zhao
  • Osmar Zaiane
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10435)


Machine learning methods have been used to predict the clinical scores and identify the image biomarkers from individual MRI scans. Recently, the multi-task learning (MTL) with sparsity-inducing norm have been widely studied to investigate the prediction power of neuroimaging measures by incorporating inherent correlations among multiple clinical cognitive measures. However, most of the existing MTL algorithms are formulated linear sparse models, in which the response (e.g., cognitive score) is a linear function of predictors (e.g., neuroimaging measures). To exploit the nonlinear relationship between the neuroimaging measures and cognitive measures, we consider that tasks to be learned share a common subset of features in the kernel space as well as the kernel functions. Specifically, we propose a multi-kernel based multi-task learning with a mixed sparsity-inducing norm to better capture the complex relationship between the cognitive scores and the neuroimaging measures. The formation can be efficiently solved by mirror-descent optimization. Experiments on the Alzheimers Disease Neuroimaging Initiative (ADNI) database showed that the proposed algorithm achieved better prediction performance than state-of-the-art linear based methods both on single MRI and multiple modalities.



This research was supported by the the National Natural Science Foundation of China (No.61502091), and the Fundamental Research Funds for the Central Universities (No.161604001, N150408001).


  1. 1.
    Argyriou, A., Evgeniou, T., Pontil, M.: Convex multi-task feature learning. Mach. Learn. 73(3), 243–272 (2008)CrossRefGoogle Scholar
  2. 2.
    Duchi, J.C., Shalev-Shwartz, S., Singer, Y., Tewari, A.: Composite objective mirror descent. In COLT, pp. 14–26 (2010)Google Scholar
  3. 3.
    Evgeniou, T., Micchelli, C.A., Pontil, M.: Learning multiple tasks with kernel methods. J. Mach. Learn. Res. 6, 615–637 (2005)MathSciNetzbMATHGoogle Scholar
  4. 4.
    Gönen, M., Alpaydin, E.: Multiple kernel learning algorithms. J. Mach. Learn. Res. 12, 2211–2268 (2011)Google Scholar
  5. 5.
    Huo, Z., Shen, D., Huang, H.: New multi-task learning model to predict Alzheimer’s disease cognitive assessment. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9900, pp. 317–325. Springer, Cham (2016). doi: 10.1007/978-3-319-46720-7_37 CrossRefGoogle Scholar
  6. 6.
    Jawanpuria, P., Nath, J.S.: Multi-task multiple kernel learning. In: Proceedings of the 2011 SIAM International Conference on Data Mining, pp. 828–838. SIAM (2011)Google Scholar
  7. 7.
    Ji, S., Ye, J.: An accelerated gradient method for trace norm minimization. In Proceedings of the 26th Annual International Conference on Machine Learning, pp. 457–464. ACM (2009)Google Scholar
  8. 8.
    Micchelli, C.A., Pontil, M.: Learning the kernel function via regularization. J. Mach. Learn. Res. 6, 1099–1125 (2005)MathSciNetzbMATHGoogle Scholar
  9. 9.
    Rakotomamonjy, A., Bach, F.R., Canu, S., Grandvalet, Y.: SimpleMKL. J. Mach. Learn. Res. 9, 2491–2521 (2008)MathSciNetzbMATHGoogle Scholar
  10. 10.
    Rakotomamonjy, A., Flamary, R., Gasso, G., Canu, S.: lp-lq penalty for sparse linear and sparse multiple kernel multitask learning. IEEE Trans. Neural Networks 22(8), 1307–1320 (2011)CrossRefGoogle Scholar
  11. 11.
    Wan, J., Zhang, Z., Rao, B.D., Fang, S., Yan, J., Saykin, A.J., Shen, L.: Identifying the neuroanatomical basis of cognitive impairment in Alzheimer’s disease by correlation-and nonlinearity-aware sparse bayesian learning. IEEE Trans. Med. Imaging 33(7), 1475–1487 (2014)CrossRefGoogle Scholar
  12. 12.
    Wan, J., Zhang, Z., Yan, J., Li, T., Rao, B.D., Fang, S., Kim, S., Risacher, S.L., Saykin, A.J., Shen, L.: Sparse bayesian multi-task learning for predicting cognitive outcomes from neuroimaging measures in Alzheimer’s disease. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 940–947 (2012)Google Scholar
  13. 13.
    Wang, H., Nie, F., Huang, H., Risacher, S., Ding, C., Saykin, A.J., Shen, L., et al.: Sparse multi-task regression and feature selection to identify brain imaging predictors for memory performance. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 557–562. IEEE (2011)Google Scholar
  14. 14.
    Yan, J., Li, T., Wang, H., Huang, H., Wan, J., Nho, K., Kim, S., Risacher, S.L., Saykin, A.J., Shen, L., et al.: Cortical surface biomarkers for predicting cognitive outcomes using group l 2, 1 norm. Neurobiol. Aging 36, S185–S193 (2015)CrossRefGoogle Scholar
  15. 15.
    Zhang, D., Shen, D., Initiative, A.D.N., et al.: 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.
    Zhou, J., Chen, J., Ye, J.: Clustered multi-task learning via alternating structure optimization. In: Advances in Neural Information Processing Systems, pp. 702–710 (2011)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Peng Cao
    • 1
    Email author
  • Xiaoli Liu
    • 1
  • Jinzhu Yang
    • 1
  • Dazhe Zhao
    • 1
  • Osmar Zaiane
    • 2
  1. 1.Key Laboratory of Medical Image Computing of Ministry of Education, College of Computer Science and EngineeringNortheastern UniversityShenyang ShiChina
  2. 2.University of AlbertaEdmontonCanada

Personalised recommendations