Advertisement

Multimedia Tools and Applications

, Volume 76, Issue 8, pp 10761–10775 | Cite as

Multi-view feature selection and classification for Alzheimer’s Disease diagnosis

  • Mingxing Zhang
  • Yang Yang
  • Fumin Shen
  • Hanwang Zhang
  • Yuan Wang
Article

Abstract

In our present society, Alzheimer’s disease (AD) is the most common dementia form in elderly people and has been a big social health problem worldwide. In this paper, we propose a novel multi-view classification method based on l 2,p -norm regularization for Alzheimer’s Disease (AD) diagnosis. Unlike the previous l 2,1 -norm regularized methods using concatenated multi-view features, we further consider the intra-structure and inter-structure relations between features of different views and use a more flexible l 2,p -norm regularization in our objective function. We also proposed a more suitable loss function to measure the loss between labels and predicted values for classification task. It experimentally demonstrated that this method enhances the performance of disease status classification, comparing to the state-of-the-art methods.

Keywords

Alzheimer’s Disease (AD) Social health Multi-view classification l2,p-norm l2,l-norm 

Notes

Acknowledgements

This work was supported in part by the National Nature Science Foundation of China under Project 61572108.

References

  1. 1.
    Brookmeyer R, Johnson E, Ziegler-Graham K et al (2007) Forecasting the global burden of Alzheimer’s disease. Alzheimers Dement 3(3):186–191CrossRefGoogle Scholar
  2. 2.
    Jie B, Zhang D, Cheng B et al (2013) Manifold regularized multi-task feature selection for multi-modality classification in Alzheimer’s disease. Medical image computing and computer-assisted intervention: MICCAI, International Conference on Medical Image Computing and Computer-Assisted Intervention Med Image Comput Comput Assist Interv 275–283Google Scholar
  3. 3.
    Liu S, Cai W, Liu S, et al. Subject-centered multi-view feature fusion for neuroimaging retrieval and classification.Google Scholar
  4. 4.
    Liu F, Suk HI, Wee CY et al (2013) High-order graph matching based feature selection for Alzheimer’s disease identification. Medical Image Computing and Computer-Assisted Intervention–MICCAI 2013. Springer Berlin Heidelberg 311–318Google Scholar
  5. 5.
    Liu F, Wee CY, Chen H et al (2014) Inter-modality relationship constrained multi-modality multi-task feature selection for Alzheimer’s Disease and mild cognitive impairment identification. Neuroimage 84:466–475CrossRefGoogle Scholar
  6. 6.
    Liu S, Zhang L, Cai W et al (2013) A supervised multiview spectral embedding method for neuroimaging classification. Image Processing (ICIP), 2013 20th IEEE International Conference on. IEEE 601–605Google Scholar
  7. 7.
    National Institute on Aging. About Alzheimer's Disease: Symptoms. https://www.nia.nih.gov/alzheimers/topics/symptoms. Retrieved 28 Dec 2011
  8. 8.
    Nie L, Akbari M, Li T, et al (2014) A joint local-global approach for medical terminology assignment. In Proceedings of the SIGIR workshop on Medical Information Retrieval (MEDIR 2014)Google Scholar
  9. 9.
    Nie L, Li T, Akbari M, et al (2014) Wenzher: comprehensive vertical search for healthcare domain. Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval. ACM 1245–1246Google Scholar
  10. 10.
    Nie L, Wang M, Zhang L, et al (2015) Disease inference from health-related questions via sparse deep learning. IEEE Trans Knowl Data Eng 27(8):2107–2119Google Scholar
  11. 11.
    Petersen RC, Doody R, Kurz A et al (2001) Current concepts in mild cognitive impairment. Arch Neurol 58(12):1985–1992CrossRefGoogle Scholar
  12. 12.
    Shi Y, Suk HI, Gao Y, et al (2014) Joint coupled-feature representation and coupled boosting for AD diagnosis. Computer Vision and Pattern Recognition (CVPR), 2014 I.E. Conference on. IEEE 2721–2728Google Scholar
  13. 13.
    Tibshirani R (1996) Regression shrinkage and selection via the lasso. J R Stat Soc Ser B 58:267–288MathSciNetMATHGoogle Scholar
  14. 14.
    Wang H, Nie F, Huang H, et al (2011) Identifying AD-sensitive and cognition-relevant imaging biomarkers via joint classification and regression. Medical Image Computing and Computer-Assisted Intervention–MICCAI 2011. Springer, Berlin Heidelberg, p 115–123Google Scholar
  15. 15.
    Yang Y, Yang Y, Huang Z, et al (2011) Tag localization with spatial correlations and joint group sparsity. Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on. IEEE 881–888Google Scholar
  16. 16.
    Yang Y, Zha Z J, Gao Y, et al (2014) Exploiting web images for semantic video indexing via robust sample-specific loss. Multimedia, IEEE Transactions on 16(6):1677–1689Google Scholar
  17. 17.
    Zhang D, Shen D (2012) Alzheimer’s Disease Neuroimaging Initiative. Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer’s disease. Neuroimage 59(2):895–907Google Scholar
  18. 18.
    Zhang M, Yang Y, Zhang H et al (2015) L2,p-norm and sample constraint based feature selection and classification for AD diagnosis. Neurocomputing (in press)Google Scholar
  19. 19.
    Zhang D, Wang Y, Zhou L, et al (2011) Multimodal classification of Alzheimer’s disease and mild cognitive impairment. Neuroimage 55(3):856–867.Google Scholar
  20. 20.
    Zhu X, Suk HI, Shen D (2014) Multi-modality canonical feature selection for Alzheimer’s disease diagnosis. Medical Image Computing and Computer-Assisted Intervention–MICCAI 2014. Springer Int Publ 162–169Google Scholar
  21. 21.
    Zhu X, Suk HI, Shen D (2014) Matrix-similarity based loss function and feature selection for alzheimer’s disease diagnosis. Computer Vision and Pattern Recognition (CVPR), 2014 I.E. Conference on. IEEE 3089–3096Google Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Mingxing Zhang
    • 1
  • Yang Yang
    • 1
  • Fumin Shen
    • 1
  • Hanwang Zhang
    • 2
  • Yuan Wang
    • 2
  1. 1.University of Electronic Science and Technology of ChinaChengduChina
  2. 2.National University of SingaporeSingaporeSingapore

Personalised recommendations