## 4 Experiments and Results
### Methodology Description and Algorithm Tuning  
We performed a first set of experiments to determine the impact of the possible settings of the algorithm, as explained next. The recordings from Spain clients were considered, and MCA was performed to generate an eigendecomposition, as described in the preceding section. Figure 1 (top, left) shows the eigenvalue profile, where it can be noted that the first three components explain most of the statistical variability, but still relevant and slowly decreasing variability is present in Components 4-12. We decided to retain six eigenvectors for their representation and three directions for the projected space representations. Note that it is common to work with the two first eigendirections for interpretation simplicity, whereas we considered that plotting projections on the first three directions could be more informative. According to the eigenvalue profile, projections on Directions 4-6 could be used if we are willing to obtain additional information, with the advantage of showing more statistical relationships but with the disadvantage of complexity for interpretation. The eigenvector representations show Directions 1-6 in decreasing eigenvalue order, and in them (left, middle and bottom panels) we can see the bootstrap confidence intervals in gray band and the significant categories in each direction (non-overlapping zero with 95% confidence intervals) are identified with a red-dotted vertical line. In the left-middle panel we can see that the first and second direction of the direct bootstrapping are very well defined, with narrow confidence bands, as it could be expected due to the high amount of recordings (several hundred thousands). However, Eigendirections 4-6 turn out to be not defined and with extremely large confidence intervals. This effect is due to the flat region of the eigenvalue profile producing eigendirection rotations during the bootstrap resampling. A procrustes correction was then included in the process to avoid this effect [52], yielding a noticeable improvement in stabilizing all these directions, as seen in the left-bottom panel, so that this option was left as the default for the analysis in the next subsection.  
The differences between using Parzen windows and SVDD for confidence volumes was also scrutinized in this example. The projections of each category was analyzed on the three first eigendirections, and it is represented for all the \(B\) resamples (in this case, \(B=1000\)), together with the confidence volumes given by each method in the same color. Panels to the right show that the direct bootstrap resampling gives more extended clouds  
Figure 1: Experiments for determining the algorithm settings. On the recordings from Spain clients, MCA was applied, yielding an eigenvalue profile (**top,left**). The six first eigendirections are represented for the bootstrapped and for the procrustes–corrected version (**left,middle,bottom**, respectively). The confidence volumes were obtained for the bootstrapped and for the procrustes-corrected versions (**right top** and **middle**, respectively). Confidence volumes with SVDD are also provided (**right,bottom**).  
of points (colored points in each bubble), whereas the use of procrustes moderates the scatter of those points. In both cases, Parzen windows were obtained to estimate the confidence volumes, and, despite the dispersion in the clouds, small differences were observed in those volumes. The right-bottom panel shows that the SVDD method provides more conservative confidence volumes compared with both precedent examples, so that they were chosen to use in the subsequent analyses.