Stability-weighted matrix completion of incomplete multi-modal data for disease diagnosis

Kim Han Thung, Ehsan Adeli, Pew Thian Yap, Dinggang Shen

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

10 Citations (Scopus)


Effective utilization of heterogeneous multi-modal data for Alzheimer’s Disease (AD) diagnosis and prognosis has always been hampered by incomplete data. One method to deal with this is low-rank matrix completion (LRMC),which simultaneous imputes missing data features and target values of interest. Although LRMC yields reasonable results,it implicitly weights features from all the modalities equally,ignoring the differences in discriminative power of features from different modalities. In this paper,we propose stability-weighted LRMC (swLRMC),an LRMC improvement that weights features and modalities according to their importance and reliability. We introduce a method,called stability weighting,to utilize subsampling techniques and outcomes from a range of hyper-parameters of sparse feature learning to obtain a stable set of weights. Incorporating these weights into LRMC,swLRMC can better account for differences in features and modalities for improving diagnosis. Experimental results confirm that the proposed method outperforms the conventional LRMC,feature-selection based LRMC,and other state-of-the-art methods.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings
EditorsGozde Unal, Sebastian Ourselin, Leo Joskowicz, Mert R. Sabuncu, William Wells
PublisherSpringer Verlag
Number of pages9
ISBN (Print)9783319467221
Publication statusPublished - 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9901 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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