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<h2>Principal Component Analysis<br>
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Principal component analysis (PCA) performs a linear transformation
of the coordinate system, so as to maximize the variance of the data
along the first principal axis of the new coordinate system.<br>
More information on <a
href="http://en.wikipedia.org/wiki/Principal_component_analysis">Wikipedia</a>.<br>
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<span style="font-weight: bold;">Components Range:</span><br>
You can choose the number of dimensions after
projection that you keep (this might be useful to reduce the
dimensionnality of the dataset for further processing)<br>
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Check the
<b>Components Range</b> box and set the desired dimensions.
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<span style="font-weight: bold;">Cumulated variance and eigenvalues</span><br
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The eigenvalues of each eigenvector and the cumulated variance
explained by the first dimensions.<br style="font-weight: bold;">
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<span style="font-weight: bold;">Recontruction error</span><br>
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