Abstract
Alzheimer’s Disease (AD) is the most common type of dementia. Identifying correct biomarkers may determine pre-symptomatic AD subjects and enable early intervention. Recently, Multi-task sparse feature learning has been successfully applied to many computer vision and biomedical informatics researches. It aims to improve the generalization performance by exploiting the shared features among different tasks. However, most of the existing algorithms are formulated as a supervised learning scheme. Its drawback is with either insufficient feature numbers or missing label information. To address these challenges, we formulate an unsupervised framework for multi-task sparse feature learning based on a novel dictionary learning algorithm. To solve the unsupervised learning problem, we propose a two-stage Multi-Source Multi-Target Dictionary Learning (MMDL) algorithm. In stage 1, we propose a multi-source dictionary learning method to utilize the common and individual sparse features in different time slots. In stage 2, supported by a rigorous theoretical analysis, we develop a multi-task learning method to solve the missing label problem. Empirical studies on an \(N = 3970\) longitudinal brain image data set, which involves 2 sources and 5 targets, demonstrate the improved prediction accuracy and speed efficiency of MMDL in comparison with other state-of-the-art algorithms.
J. Zhang and Q. Li—These two authors contributed equally to this work.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Argyriou, A., Evgeniou, T., Pontil, M.: Convex multi-task feature learning. Mach. Learn. 73(3), 243–272 (2008)
Boureau, Y.L., Ponce, J., LeCun, Y.: A theoretical analysis of feature pooling in visual recognition. In: Proceedings of the 27th Annual ICML, pp. 111–118 (2010)
Canutescu, A.A., Dunbrack, R.L.: Cyclic coordinate descent: a robotics algorithm for protein loop closure. Protein Sci. 12(5), 963–972 (2003)
Chen, J., et al.: A convex formulation for learning shared structures from multiple tasks. In: Proceedings of the 26th Annual ICML, pp. 137–144. ACM (2009)
Combettes, P.L., Wajs, V.R.: Signal recovery by proximal forward-backward splitting. Multiscale Model. Simul. 4(4), 1168–1200 (2005)
Evgeniou, T., Micchelli, C.A., Pontil, M.: Learning multiple tasks with kernel methods. J. Mach. Learn. Res. 6, 615–637 (2005)
Hoerl, A.E., Kennard, R.W.: Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12(1), 55–67 (1970)
Lee, H., Battle, A., Raina, R., Ng, A.Y.: Efficient sparse coding algorithms. In: Advances in Neural Information Processing Systems, pp. 801–808 (2006)
Lin, B., et al.: Stochastic coordinate coding and its application for drosophila gene expression pattern annotation. arXiv preprint arXiv:1407.8147 (2014)
Lv, J., et al.: Task fMRI data analysis based on supervised stochastic coordinate coding. Med. Image Anal. 38, 1–16 (2017)
Mairal, J., Bach, F., Ponce, J., Sapiro, G.: Online dictionary learning for sparse coding. In: Proceedings of the 26th Annual ICML, pp. 689–696. ACM (2009)
Maurer, A., Pontil, M., Romera-Paredes, B.: Sparse coding for multitask and transfer learning. In: Proceedings of the 26th Annual ICML 2013, Atlanta, GA, USA, 16–21 June 2013, pp. 343–351 (2013)
Tibshirani, R.: Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Ser. B (Methodol.) 58(1), 267–288 (1996)
Wang, H., et al.: Sparse multi-task regression and feature selection to identify brain imaging predictors for memory performance. In: ICCV, pp. 557–562. IEEE (2011)
Wang, Y., et al.: Surface-based TBM boosts power to detect disease effects on the brain: an N = 804 ADNI study. Neuroimage 56(4), 1993–2010 (2011)
Xiang, S., et al.: Bi-level multi-source learning for heterogeneous block-wise missing data. NeuroImage 102, 192–206 (2014)
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)
Zhang, J., et al.: Hyperbolic space sparse coding with its application on prediction of Alzheimer’s disease in mild cognitive impairment. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9900, pp. 326–334. Springer, Cham (2016). doi:10.1007/978-3-319-46720-7_38
Zhang, J., et al.: Applying sparse coding to surface multivariate tensor-based morphometry to predict future cognitive decline. In: 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), pp. 646–650. IEEE (2016)
Zhang, T.: Solving large scale linear prediction problems using stochastic gradient descent algorithms. In: Proceedings of the 25th Annual ICML, p. 116. ACM (2004)
Zhou, J., Liu, J., Narayan, V.A., Ye, J.: Modeling disease progression via fused sparse group lasso. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1095–1103. ACM (2012)
Acknowledgments
The research was supported in part by NIH (R21AG049216, RF1AG051710, U54EB020403) and NSF (DMS-1413417, IIS-1421165).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Zhang, J., Li, Q., Caselli, R.J., Thompson, P.M., Ye, J., Wang, Y. (2017). Multi-source Multi-target Dictionary Learning for Prediction of Cognitive Decline. In: Niethammer, M., et al. Information Processing in Medical Imaging. IPMI 2017. Lecture Notes in Computer Science(), vol 10265. Springer, Cham. https://doi.org/10.1007/978-3-319-59050-9_15
Download citation
DOI: https://doi.org/10.1007/978-3-319-59050-9_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-59049-3
Online ISBN: 978-3-319-59050-9
eBook Packages: Computer ScienceComputer Science (R0)