PCA

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(What works well)
(What works well)
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== What works well ==
== What works well ==
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If you are training your algorithm on images other than natural images (for example, images of handwritten characters, or images of single isolated objects centered against a white background), other types of normalization might be worth considering, and the best choice may be application dependent. But when training on natural images, using the per-image mean normalization as the normalization equations above would be a reasonable default.
 
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For PCA to work, usually we want each of the features <math>\textstyle x_1, x_2, \ldots, x_n</math>
For PCA to work, usually we want each of the features <math>\textstyle x_1, x_2, \ldots, x_n</math>
to have a similar range of values to the others (and to have a mean close to
to have a similar range of values to the others (and to have a mean close to
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and that <math>\textstyle \mu^{(i)}</math> here is the mean intensity of the image <math>\textstyle x^{(i)}</math>.  In particular,
and that <math>\textstyle \mu^{(i)}</math> here is the mean intensity of the image <math>\textstyle x^{(i)}</math>.  In particular,
this is not the same thing as estimating a mean value separately for each pixel <math>\textstyle x_j</math>.
this is not the same thing as estimating a mean value separately for each pixel <math>\textstyle x_j</math>.
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 +
If you are training your algorithm on images other than natural images (for example, images of handwritten characters, or images of single isolated objects centered against a white background), other types of normalization might be worth considering, and the best choice may be application dependent. But when training on natural images, using the per-image mean normalization as the normalization equations above would be a reasonable default.
== Non-natural images ==
== Non-natural images ==

Revision as of 19:51, 4 April 2011

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