NDLI: Independent component analysis by convex divergence minimization: applications to brain fMRI analysis

Content Provider	IEEE Xplore Digital Library
Author	Matsuyama, Y. Imahara, S.
Copyright Year	2001
Description	Author affiliation: Dept. of Electr., Electron. & Comput. Eng., Waseda Univ., Tokyo, Japan (Matsuyama, Y.)
Abstract	A class of independent component analysis (ICA) algorithms using a minimization of the convex divergence, called the f-ICA, is presented. This algorithm is a super class of the minimum mutual information ICA and our own /spl alpha/-ICA. The following properties are obtained: 1) the f-ICA can be implemented by both momentum and turbo methods, and their combination is also possible; 2) the formerly presented /spl alpha/-ICA can claim an equivalent form to the f-ICA if the design parameter /spl alpha/ is chosen appropriately; 3) the f-ICA is much faster than the minimum mutual information ICA; and 4) additional complexity required to the divergence ICA is light, and thus this algorithm is applicable to a large amount of data via conventional personal computers. Detection of human brain areas that strongly respond to moving objects is reported in this paper.
Sponsorship	Int. Neural Network Soc.
Starting Page	412
Ending Page	417
File Size	541992
Page Count	6
File Format	PDF
ISBN	0780370449
ISSN	10987576
DOI	10.1109/IJCNN.2001.939055
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2001-07-15
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Independent component analysis Mutual information Application software Minimization methods Convergence Magnetic resonance imaging Algorithm design and analysis Object detection Humans Brain
Content Type	Text
Resource Type	Article

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