A constrained alternating least squares nonnegative matrix factorization algorithm enhances task-related neuronal activity detection from single subject's fMRI data

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

6 Citations (Scopus)

Abstract

This paper proposes a constrained alternating least squares nonnegative matrix factorization algorithm (cALSNMF) to enhance alternating least squares non-negative matrix factorization (ALSNMF) in detecting task-related neuronal activity from single subject's fMRI data. In cALSNMF, a new cost function is defined in consideration of the uncorrelation and overdeter-mined problems of fMRI data, A novel training procedure is generated by combining optimal brain surgeon (OBS) algorithm in weight updating process, which considers the interaction among voxels. The experiments on both simulated data and fMRI data show that cALSNMF fits data better without any prior information and works more adaptively than original ALSNMF on detecting task-related neuronal activity.

Original languageEnglish
Title of host publicationProceedings of 2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011
Pages338-343
Number of pages6
DOIs
Publication statusPublished - 2011
Event2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011 - Guilin, Guangxi, China
Duration: 2011 Jul 102011 Jul 13

Publication series

NameProceedings - International Conference on Machine Learning and Cybernetics
Volume1
ISSN (Print)2160-133X
ISSN (Electronic)2160-1348

Other

Other2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011
CountryChina
CityGuilin, Guangxi
Period11/7/1011/7/13

Keywords

  • Constrained alternating least squares nonnegative matrix factorization
  • fMRI
  • optimal brain surgeon

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Computer Networks and Communications
  • Human-Computer Interaction

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