# Semantically Controlled Adaptive Equalisation in Reduced Dimensionality Parameter Space +
## 5 Results
### User Evaluation  
We evaluate the performance of the selected mode (sAE) using subjective tests in which we present the user with various samples and ask them to equalise it using the low-dimensional space (shown in Figure 6). We then measure the class separability using the JMD metric presented in Section 3.2. In Table 7 we present the degree of separation between user inputs using high-dimensional and low-dimensional responses from the subjective data. From this we can deduce that the overlap between _warm_ and _bright_ descriptors has decreased, with a value of 0.8527. This is higher than the high-dimensional dataset instances (0.5581). Furthermore, we see an increase in separation between the high-dimensional classes and the opposing low-dimensional classes. For instance, the high-dimensional _warm_ examples and the low-dimensional _bright_ examples achieve a separation of 0.7719, again higher than the original separation between the high-dimensional classes. Similarly, a strong positive correlation between high-dimensional and low-dimensional equalisation is exhibited by examples in the same class, a desired effect that displays the ability of the users to choose the corresponding regions for the two descriptors.  
This is reinforced by the low Euclidean distances between class centroids (shown in Figure 6) and strong positive coherence (spectral correlation) between the equaliser curves achieved using the 13  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Technique** & **Trustworthiness** & **Continuity** & **1-NN Classification** \\ \hline Original & - & - & 84.9\% \\ PCA & 0.8463 & 0.9562 & 67.85\% \\ pPCA & 0.8454 & 0.9552 & 67.39\% \\ kPCA & 0.8263 & 0.9566 & 69.40\% \\ FA & 0.7761 & 0.9359 & 59.52\% \\ DM & 0.8477 & 0.9561 & 66.03\% \\ LDA & 0.6702 & 0.8340 & 73.92\% \\ sAE(3-Layer) & 0.8440 & 0.9431 & 73.51\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: _Trustworthiness_ and _continuity_ scores (including weighting) for the different dimensionality reduction techniques (higher values are better), and classification accuracy of 1-nn classification  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Separability** & **W(13-d)/B(13-d)** & **W(2-d)/B(2-d)** & **W(13-d)/B(2-d)** & **B(13-d)/W(2-d)** & **W(13-d)/W(2-d)** & **B(13-d)/B(2-d)** \\ \hline JMD & 0.5581 & 0.8527 & 0.7719 & 0.6988 & 0.0846 & 0.1439 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Jeffries-Matusita Distance (JMD) scores showing separation for data gathered from 13-dimensional parameters and a two-dimensional interface using _warm_(W) and _bright_(B) examples. Higher scores are desirable for the first four measurements, while lower scores are better for the last two columns.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Metric** & **B(13-d)/B(2-d)** & **W(13-d)/W(2-d)** & **W(13-d)/B(13-d)** & **W(2-d)/B(2-d)** \\ \hline Pearson correlation & 0.9346 & 0.9247 & -0.7594 & -0.9121 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Pearson correlation between the reconstructed equaliser curves.  
and 2-dimensional interfaces (shown in Figure 7a,b). These results are provided through the Pearson correlation measures in Table 8, revealing a positive correlation between the high-dimensional and low-dimensional datasets for the same descriptor: 0.9346 for warm and 0.9247 for bright, and a negative correlation between opposite high-dimensional and low dimensional descriptors: \(-\)0.7594 and \(-\)0.9121, respectively.