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@@ -992,7 +992,7 @@ <h3 id="sparse-coding-or-dictionary-learning">Sparse Coding (or Dictionary Learn
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 where <span class="arithmatex">\(\beta\)</span>, <span class="arithmatex">\(\lambda\)</span> are directly related to the parameters of the posterior distribution that we wish to maximize. This
-functional can be minimized in an alternating fashion, first for <span class="arithmatex">\(\mathbf{W}\)</span>, then for <span class="arithmatex">\(\mathbf{x}\)</span>, and so on and so forth.</p>
+functional can be minimized in an alternating fashion, first for <span class="arithmatex">\(\mathbf{W}\)</span>, then for <span class="arithmatex">\(\mathbf{c}\)</span>, and so on and so forth.</p>
 <p>Finally, once the training process is over and <span class="arithmatex">\(\hat{\mathbf{W}}\)</span> is available, it is worth noting that sparse coding does require
 solving a sparsity-promoting inverse problem for any new training sample <span class="arithmatex">\(\mathbf{x}\)</span> in order to find its best
 representation <span class="arithmatex">\(\hat{\mathbf{c}}\)</span>. Nevertheless, despite the higher cost compared to for example PCA, sparse coding has shown