Self-Taught Learning

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(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 ==  

Revision as of 23:30, 10 May 2011

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