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. 2013 Jan 24;9(1):e1002873. doi: 10.1371/journal.pcbi.1002873

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© 2013 Gerhard et al

This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.

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A. A set of 64 pixel natural image patches, . B. The coefficients of the first two (non-DC) ICA components are plotted against each other for all 64 patches along with their marginal distributions. C. Histogram of the 64 patches' norms in the ICA basis. D. To apply the ICA independence assumption to , we shuffle the ICA coefficients across samples separately for each component. Shown are the resulting matched model patches, . E. The coefficients of the first two (non-DC) ICA components of . The marginal distributions are the same as those of shown in B. F. Histogram of the coefficient norms of the 64 patches in . Applying the ICA assumption has changed the radial distribution so that the variance is much lower than that of the original distribution shown in C.

Inline graphic — A. A set of 64 pixel natural image patches, . B. The coefficients of the first two (non-DC) ICA components are plotted against each other for all 64 patches along with their marginal distributions. C. Histogram of the 64 patches' norms in the ICA basis. D. To apply the ICA independence assumption to , we shuffle the ICA coefficients across samples separately for each component. Shown are the resulting matched model patches, . E. The coefficients of the first two (non-DC) ICA components of . The marginal distributions are the same as those of shown in B. F. Histogram of the coefficient norms of the 64 patches in . Applying the ICA assumption has changed the radial distribution so that the variance is much lower than that of the original distribution shown in C.