title: Principal component analysis in matlab?
body: <p>I have a training set with the size of (<code>size(X_Training)=122 x 125937</code>). </p>
  
  <ul>
  <li><code>122</code> is the number of features</li>
  <li>and <code>125937</code> is the sample size.</li>
  </ul>
  
  <p>From my little understanding, PCA is useful when you want to reduce the dimension of the features. Meaning, I should reduce 122 to a smaller number.</p>
  
  <p>But when I use in matlab:</p>
  
  <pre><code>X_new = pca(X_Training)
  </code></pre>
  
  <p>I get a matrix of size <code>125973x121</code>, I am really confused, because this not only changes the features but also the sample size? This is a big problem for me, because I still have the target vector <code>Y_Training</code> that I want to use for my neural network.</p>
  
  <p>Any help? Did I badly interpret the results? I only want to reduce the number of features.</p>