期刊
PLOS ONE
卷 8, 期 8, 页码 -出版社
PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pone.0073309
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资金
- National Institutes of Health [2R01EB000840, 5P20RR021938]
- Direct For Computer & Info Scie & Enginr
- Division of Computing and Communication Foundations [1117056] Funding Source: National Science Foundation
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [1017718] Funding Source: National Science Foundation
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 the ICA algorithms are indeed doing what they are designed to do: identify maximally independent sources.
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