Candidates for Synergies: Linear Discriminants versus Principal Components

<table>Comparison between PCA and ULDA on one sample data set. (a) Two-variable data was analyzed using PCA. The figure shows a biplot of principal components. The maximum variance is across the horizontal axis or PC1. The second highest variance is across the vertical axis or PC2. (b) The figure shows the linear discriminant that separates the two groups of data. The axes represent the two variables, <svg height="15.25" id="M1" style="vertical-align:-3.13504pt" version="1.1" viewbox="0 0 19.924999 15.25" width="19.924999" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink">
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Computational Intelligence and Neuroscience

fig1

Figure 1

Figure 1: Candidates for Synergies: Linear Discriminants versus Principal Components 