Self-Taught Learning
From Ufldl
(→Learning features) |
(→On pre-processing the data) |
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various pre-processing parameters. For example, one may have computed | various pre-processing parameters. For example, one may have computed | ||
a mean value of the data and subtracted off this mean to perform mean normalization, | a mean value of the data and subtracted off this mean to perform mean normalization, | ||
- | or used PCA to compute a matrix <math>\textstyle U</math> to represent the data as <math>\textstyle U^Tx</math> (or PCA | + | or used PCA to compute a matrix <math>\textstyle U</math> to represent the data as <math>\textstyle U^Tx</math> (or used |
+ | PCA | ||
whitening or ZCA whitening). If this is the case, then it is important to | whitening or ZCA whitening). If this is the case, then it is important to | ||
save away these preprocessing parameters, and to use the ''same'' parameters | save away these preprocessing parameters, and to use the ''same'' parameters | ||
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labeled training set, since that might result in a dramatically different | labeled training set, since that might result in a dramatically different | ||
pre-processing transformation, which would make the input distribution to | pre-processing transformation, which would make the input distribution to | ||
- | the autoencoder very different from what it was actually trained on. | + | the autoencoder very different from what it was actually trained on. |
== On the terminology of unsupervised feature learning == | == On the terminology of unsupervised feature learning == |