Sparse Multi-kernel Based Multi-task Learning for Joint Prediction of Clinical Scores and Biomarker Identification in Alzheimer’s Disease
- 9k Downloads
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).
- 2.Duchi, J.C., Shalev-Shwartz, S., Singer, Y., Tewari, A.: Composite objective mirror descent. In COLT, pp. 14–26 (2010)Google Scholar
- 4.Gönen, M., Alpaydin, E.: Multiple kernel learning algorithms. J. Mach. Learn. Res. 12, 2211–2268 (2011)Google Scholar
- 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.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.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
- 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.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
- 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