# A Two-Stage Semi-Supervised High Maneuvering Target Trajectory Data Classification Algorithm
## 4 Experiment and Analysis
### Label Data Category Missing Problem  
The processing capability for labeled categories has been verified in the previous section. This section further examines the clustering ability of the algorithm for unlabeled categories in the target dataset. For categories with missing labels, the classification model could effectively classify, and the classification accuracy was greatly reduced, so this section will no longer use the label propagation and GRU classification model as the comparison algorithm. In our algorithm, the processing ability of unlabeled categories in the dataset mainly came from the designed two-stage training method. Therefore, the conventional Siamese neural network training method that does not perform clustering pre-training and directly uses label data to train was selected as a comparison. We created simulation datasets for the experiments. The condition of missing categories in the labeled dataset was simulated by removing the entire class of data in the labeled dataset. First, under the conditions of the same learning rate and the number of iterations, we compared the training processes of the two methods in the categories of complete label and missing label datasets, as shown in Figure 12.  
From the loss curve plotted in Figure 12, it can be seen that in the labeled dataset with complete categories, both algorithms could obtain lower losses through training. However, the training process of the model directly trained was unstable, and the loss of final convergence was larger. The gap between the test set and training set results was larger, indicating that it was more prone to overfitting. In contrast, the initial loss in the training process of the two-stage model designed in this study was smaller, so the training convergence was faster. This advantage was even more pronounced on incomplete labeled datasets. The above results showed that the two-stage fine-tuning method designed in this paper was beneficial to the training of the model and was more robust in the problem of missing categories. After that, datasets with different proportions of missing labels were made, the missing categories were selected by cross-validation, and the average value was obtained after multiple experiments. Table 4 shows the clustering results of the above two algorithms under different missing ratios in the statistical label dataset. As can be seen from Table 4, as the number of missing categories in label data increased, the performance of the models trained by the two training methods continuously decreased. However, the two-stage fine-tuning model designed in this study eventually converged in the performance of the general clustering model, while the directly trained model fell all the way and lost the ability to classify normally. This result proved that the algorithm in this study successfully combined the clustering and classification models, which ensured that the label data was used to improve the classification effect of the model without losing the previous clustering performance of the model. In addition, from the results of 100% category completeness in Table 4, it can be seen that the network trained in two stages still has a better classification effect in the absence of the missing category problem. This was because the training method based on the clustering model had better initial weights and could find the optimal solution during training more quickly and accurately, while the direct training method initially required more weight adjustment, and the training was more difficult. In order to compare the classification effect of the algorithm on the unlabeled categories in the dataset more intuitively, the clustering purity of the two classification algorithms for each category of trajectories was plotted, as shown in Figure 13. There were two classes of labels missing from the label dataset, where class 11 and class 12 corresponded to the classes for which labels were removed.