Kernel-based multi-task joint sparse classification for Alzheimer'S disease

Yaping Wang, Manhua Liu, Lei Guo, Dinggang Shen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Citations (Scopus)

Abstract

Multi-modality imaging provides complementary information for diagnosis of neurodegenerative disorders such as Alzheimer's disease (AD) and its prodrome, mild cognitive impairment (MCI). In this paper, we propose a kernel-based multi-task sparse representation model to combine the strengths of MRI and PET imaging features for improved classification of AD. Sparse representation based classification seeks to represent the testing data with a sparse linear combination of training data. Here, our approach allows information from different imaging modalities to be used for enforcing class level joint sparsity via multi-task learning. Thus the common most representative classes in the training samples for all modalities are jointly selected to reconstruct the testing sample. We further improve the discriminatory power by extending the framework to the reproducing kernel Hilbert space (RKHS) so that nonlinearity in the features can be captured for better classification. Experiments on Alzheimer's Disease Neuroimaging Initiative database shows that our proposed method can achieve 93.3% and 78.9% accuracy for classification of AD and MCI from healthy controls, respectively, demonstrating promising performance in AD study.

Original languageEnglish
Title of host publicationISBI 2013 - 2013 IEEE 10th International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro
Pages1364-1367
Number of pages4
DOIs
Publication statusPublished - 2013
Event2013 IEEE 10th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2013 - San Francisco, CA, United States
Duration: 2013 Apr 72013 Apr 11

Publication series

NameProceedings - International Symposium on Biomedical Imaging
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Other

Other2013 IEEE 10th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2013
CountryUnited States
CitySan Francisco, CA
Period13/4/713/4/11

Keywords

  • Alzheimer's disease (AD)
  • Kernel-based classification
  • Multi-task joint sparse representation
  • Sparse representation based classifier

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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  • Cite this

    Wang, Y., Liu, M., Guo, L., & Shen, D. (2013). Kernel-based multi-task joint sparse classification for Alzheimer'S disease. In ISBI 2013 - 2013 IEEE 10th International Symposium on Biomedical Imaging: From Nano to Macro (pp. 1364-1367). [6556786] (Proceedings - International Symposium on Biomedical Imaging). https://doi.org/10.1109/ISBI.2013.6556786