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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)

Abstract

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.

Notes

Acknowledgment

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).

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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

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