Correction: Independent Component Analysis for Brain fMRI Does Indeed Select for Maximal Independence
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چکیده
منابع مشابه
Independent Component Analysis for Brain fMRI Does Indeed Select for Maximal Independence
A recent paper by Daubechies et al. claims that two independent component analysis (ICA) algorithms, Infomax and FastICA, which are widely used for functional magnetic resonance imaging (fMRI) analysis, select for sparsity rather than independence. The argument was supported by a series of experiments on synthetic data. We show that these experiments fall short of proving this claim and that th...
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InfoMax and FastICA are the independent component analysis algorithms most used and apparently most effective for brain fMRI. We show that this is linked to their ability to handle effectively sparse components rather than independent components as such. The mathematical design of better analysis tools for brain fMRI should thus emphasize other mathematical characteristics than independence.
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ژورنال
عنوان ژورنال: PLoS ONE
سال: 2013
ISSN: 1932-6203
DOI: 10.1371/annotation/52c7b854-2d52-4b49-9f9f-6560830f9428