## 2 Methods
### Graph Embedding
### Parameter Sensitivity Analysis

We conducted parameter sensitivity analysis, which indicated that the proposed method achieved an optimal recognition performance with a wide range of parameter values. We plot the recognition accuracies of different parameters in Figure 4. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Category** & **NN** & **PCA** & **TCA** & **JDA** & **GFK** & **SJDA** & **DSTL** & **JSTL** \\ \hline Healthy & 63.33 & 58.96 & 68.09 & 70.08 & 64.36 & 70.39 & 68.14 & **75.37** \\ Neuromuscular & 39.10 & 33.00 & 43.90 & **47.54** & 40.60 & 45.23 & 43.59 & 37.96 \\ Structural & 40.59 & 36.66 & 36.43 & 35.25 & 40.22 & 37.97 & 38.55 & **62.96** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Average accuracy (%) of different categories of different methods. Figure 3: Confusion matrices of the proposed method under the six settings. With the parameter \(\lambda\) varying from 0.001 to 100, the overall recognition rate had a downward trend. Meanwhile, when \(\lambda\) = 0.01, most of the settings achieved the highest accuracy. With the parameter \(\mu\) varying from 0.001 to 100, with the increase in the value of \(\mu\), the recognition rate also increased, and five settings achieved the highest accuracy when \(\mu\) = 10. With the parameter \(k\) varying from 2 to 8, when the value of K was small, the \(k\)-nearest neighbor model was more complicated, and it was prone to overfitting; when the \(k\) value was larger, the \(k\)-nearest neighbor model was simpler. When \(k\) = 4, four settings achieved the highest accuracy. ### Feature Visualization

Before using transfer learning, the top three features from the MEEI and SVD were selected by PCA [40], respectively, which were named PCA1, PCA2, and PCA3. Those chosen from our method were named F1, F2, F3, which can be seen in Figure 5. From Figure 5a,b, it is obvious that the feature distributions from the MEEI and SVD were quite different. For the MEEI, the feature distribution was relatively sparse, and the points were mostly concentrated at the bottom. For the SVD, the feature distribution was more even, but the points were all clustered together. Furthermore, if we take PCA3 as an example, the value of the MEEI varied from \(-\)0.2 to 0.4, while the value of the SVD varied from \(-\)0.2 to 0.1. However, in Figure 5c,d, the feature distributions in the MEEI and SVD were similar. This demonstrates that transfer learning methods can effectively eliminate the discrepancy between the source and the target domain. Figure 4: Parameter sensitivity analysis. ## 5 Conclusions

To improve the performance of cross-database pathological voice recognition, joint subspace transfer learning was proposed. This method took conditional distribution into account. Furthermore, intra-class distance, inter-class distance, and graph matrix acted as regularization, which guaranteed the maximal separability among databases and preserved the local structural consistencies over labels. The average recognition rate of our proposed method was 9.92% higher than that of SJDA, which demonstrates that the graph embedding matrix effectively maintained the structure of the label and guaranteed the robustness of the model in the iterative process. At the same time, the recognition rate of our method was 10.39% higher than that of DSTL. It can be concluded that conditional feature distributions should be given more attention, as they effectively eliminated the discrepancy between the source and target domain. For six cross-database experiment settings, the H-M setting achieved the highest improvement, with a 15% increase, and the H-S setting achieved the lowest improvement, with a 2.5% increase. For different voice categories, the categories of structural voice and healthy voice tested by proposed method showed a higher accuracy than the other transfer learning methods. Finally, three dimensional scatter plots were drawn to observe the feature distribution of samples. It is obvious that the feature distribution became similar after the transfer learning methods.