PCA
From Ufldl
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== What works well == | == What works well == | ||
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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 == |