Clustering-induced multi-task learning for AD/MCI classification

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

3 Citations (Scopus)

Abstract

In this work, we formulate a clustering-induced multi-task learning method for feature selection in Alzheimer's Disease (AD) or Mild Cognitive Impairment (MCI) diagnosis. Unlike the previous methods that often assumed a unimodal data distribution, we take into account the underlying multipeak distribution of classes. The rationale for our approach is that it is likely for neuroimaging data to have multiple peaks or modes in distribution due to the inter-subject variability. In this regard, we use a clustering method to discover the multipeak distributional characteristics and define subclasses based on the clustering results, in which each cluster covers a peak. We then encode the respective subclasses, i.e., clusters, with their unique codes by imposing the subclasses of the same original class close to each other and those of different original classes distinct from each other. We finally formulate a multi-task learning problem in an ℓ2,1-penalized regression framework by taking the codes as new label vectors of our training samples, through which we select features for classification. In our experimental results on the ADNI dataset, we validated the effectiveness of the proposed method by achieving the maximal classification accuracies of 95.18% (AD/Normal Control: NC), 79.52% (MCI/NC), and 72.02% (MCI converter/MCI non-converter), outperforming the competing single-task learning method.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention, MICCAI 2014 - 17th International Conference, Proceedings
PublisherSpringer Verlag
Pages393-400
Number of pages8
EditionPART 3
ISBN (Print)9783319104423
DOIs
Publication statusPublished - 2014
Event17th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014 - Boston, MA, United States
Duration: 2014 Sep 142014 Sep 18

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 3
Volume8675 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other17th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014
CountryUnited States
CityBoston, MA
Period14/9/1414/9/18

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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