PCA
From Ufldl
(→What works well) |
(→What works well) |
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== What works well == | == What works well == | ||
| + | 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. | ||
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> | ||
