# A Two-Stage Semi-Supervised High Maneuvering Target Trajectory Data Classification Algorithm
## 4 Experiment and Analysis
### Comprehensive Performance Analysis  
From the results and analysis of Tables 3 and 4 in the previous two sections, the algorithm's ability to deal with the problem of insufficient data and missing categories could be generally judged. In order to more intuitively observe the working performance of the algorithm under the condition that there were two kinds of problems at the same time, we used the amount of label data and the completeness rate of label category as the x- and y-coordinates and the purity as the z-axis, and drew a 3-dimensional surface graph, as shown in Figure 14.  
Figure 14: Purity surface plot. Figure 13: Classification accuracy of various types of data. (**a**) Directly Trained model; (**b**) two-stage trained model. Among them, the specific values of the purity results of the two algorithms are shown in Table 5.  
The specific result data under the condition that the amount of each label data was 5 and 25% of label categories were missing are shown in Table 6. In addition, the directly trained Siamese neural network and the method of combining the GRU network classification model and autoencoder clustering were used. That is, we first classified the untrained categories as abnormal categories and then used the autoencoder to cluster the abnormal categories as a comparison algorithm. It can be seen from the data in Table 6 that compared with other semi-supervised algorithms, the model based on the Siamese neural network had obvious advantages. Combining the conclusions of the previous two sections, it was judged that this advantage was mainly reflected in the ability to deal with the problem of missing categories. Figure 14 shows the comparison of the two methods based on a Siamese neural network. The ordinate in Figure 14 is the clustering purity; the red surface corresponds to the purity change of the algorithm in this study; the blue surface corresponds to the directly trained Siamese neural network; the gray plane represents the unlabeled clustering purity, which is stable at around 0.8. It can be seen from the figure that the two surfaces have no obvious change in the coordinate axis of the data amount, indicating that the two methods both have high robustness in this problem, which was due to the Siamese network effectively dealing with the problem of few labels. However, for the problem of missing categories, the processing capabilities of the two algorithms were significantly different. In the figure, the purity surface of the algorithm in this study always remained above the direct clustering effect, and the classification accuracy of the directly trained Siamese network model was even lower than that of the direct clustering when the missing class rate was high. It showed that the two-stage training method designed in this study could better maintain the clustering effect of the model. It was more robust in the missing class problem. Compared with the directly trained Siamese network, the two-stage training method designed in this study was more effective, especially in the KL divergence that reflected the overall distribution of the overall clustering results. This was because the method retained the \begin{table}
\begin{tabular}{c c c} \hline \hline
**Clustering Algorithm** & **Purity** & **KL Divergence** \\ \hline GRU migration model + autoencoder clustering & 0.821 & 0.104 \\ Label propagation algorithm & 0.735 & 0.158 \\ Direct training (Siamese network) & 0.832 & 0.115 \\ Algorithm in this study & 0.885 & 0.077 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Clustering results.