Sparse Coding: Autoencoder Interpretation

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m (Batching examples into mini-batches)
(Good initialization of s)
Line 114: Line 114:
<ol>
<ol>
<li>Set <math>s \leftarrow W^Tx</math> (where <math>x</math> is the matrix of patches in the mini-batch)
<li>Set <math>s \leftarrow W^Tx</math> (where <math>x</math> is the matrix of patches in the mini-batch)
-
<li>For each feature in <math>s</math> (i.e. each column of <math>s</math>), divide the feature by the norm of the corresponding basis in <math>A</math>. That is, if <math>s_{r, c}</math> is the <math>r</math>th feature for the <math>c</math>th example, and <math>A_c</math> is the <math>c</math>th basis vector in <math>A</math>, then set <math>s_{r, c} \leftarrow \frac{ s_{r, c} } { \lVert A_c \rVert }.</math>
+
<li>For each feature in <math>s</math> (i.e. each column of <math>s</math>), divide the feature by the norm of the corresponding basis vector in <math>A</math>. That is, if <math>s_{r, c}</math> is the <math>r</math>th feature for the <math>c</math>th example, and <math>A_c</math> is the <math>c</math>th basis vector in <math>A</math>, then set <math>s_{r, c} \leftarrow \frac{ s_{r, c} } { \lVert A_c \rVert }.</math>
</ol>
</ol>

Revision as of 06:52, 29 May 2011

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