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

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 fu...

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Vydané v:	2011 International Conference on Machine Learning and Cybernetics Ročník 1; s. 338 - 343
Hlavní autori:	Xiaoyu Ding, Jong-Hwan Lee, Seong-Whan Lee
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 01.07.2011
Predmet:	Constrained alternating least squares nonnegative matrix factorization fMRI Magnetic resonance imaging optimal brain surgeon
ISBN:	9781457703058, 145770305X
ISSN:	2160-133X
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Shrnutí:	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.
ISBN:	9781457703058 145770305X
ISSN:	2160-133X
DOI:	10.1109/ICMLC.2011.6016680