Summary of σ parameter for rbf kernel function.

<p>Summary of σ parameter for rbf kernel function.</p

Schematic example of: (a) “regression coefficients” of original variables trajectories plotted versus their range; (b) the maximum absolute value of “regression coefficients” of original variables trajectories shown in a.

<p>Schematic example of: (a) “regression coefficients” of original variables trajectories plotted versus their range; (b) the maximum absolute value of “regression coefficients” of original variables trajectories shown in a.</p

Loading plot of pseudo samples trajectories for selected variables.

<p>Numbers in the brackets correspond to variable numbers in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0038163#pone-0038163-g004" target="_blank">Figure 4</a>.</p

Conceptual flowchart of kernel-based data fusion.

<p>X₁ and X₂ are two blocks of data. *Note that all optimized parameters, i.e. number of variables, sigma for the rbf kernel, coefficients µ and nr. of LV’s are kept during the model reconstruction using all available samples. The particular steps are described in sections data analysis.</p

Representations of the a) kernel mapping of data matrix X into kernel space; b) pseudo samples principle in K-PLS-DA.

<p>k indicates the range of pseudo sample values (uniformly distributed); *Note that there are “p” pseudo sample matrixes and “p” kernel pseudo samples matrixes. **The ŷ-values can be projected into latent variable space. ^#Note that for kernel pseudo samples the loading and <b>b</b> vector of K-PLS-DA model are used. ***These ŷ-values can be represented as “regression coefficients” shown later in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0038163#pone-0038163-g004" target="_blank">Figure 4</a> or loading plot shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0038163#pone-0038163-g005" target="_blank">Figure 5</a>.</p

The maximum absolute value of “regression coefficients” of original variables.

<p>The maximum absolute value of “regression coefficients” of original variables.</p

The number of samples included in a training and independent test set.

<p>The number of samples included in a training and independent test set.</p