# A Two-Stage Semi-Supervised High Maneuvering Target Trajectory Data Classification Algorithm
## 4 Experiment and Analysis
### Label Data Amount Insufficient Problem
The statistical clustering effects of the above algorithms are shown in Table 3.  
The model for direct clustering and the classification model trained with a small number of labels performed the worst. The reason is that both algorithms do not make full use of known information and only use unlabeled data or labeled data, respectively. The effect of the classification model trained under the condition of few labels was even worse than the clustering model, indicating that the amount of label data under this experimental condition was too small to reflect the characteristics of the dataset, and the classification model could not be effectively trained. The performance of the two semi-supervised classification algorithms was significantly improved. The GRU migration model algorithm made full use of the pre-training information provided by unlabeled data, which effectively reduced the difficulty of training the classification model and alleviated the problems caused by insufficient labeled data, but this method had limitations. As shown in Table 3, under the extreme condition that the amount of all kinds of label data was 1, too little label data brought negative effects to the original clustering model, which reduced the performance compared with the clustering algorithm. The label propagation algorithm also required the number of labels. Under the condition that the amount of label data was 1, this method was essentially equivalent to the k-means algorithm for determining the initial cluster center. The effect of two semi-supervised classification algorithms, label propagation and transfer training, was  
Figure 11: Comparison of training loss curves. (**a**) LSTM classification model training and (**b**) our algorithm training. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Clustering Algorithm**} & \multicolumn{2}{c}{**The Labels’ Number Is 5**} & \multicolumn{2}{c}{**The Labels’ Number Is 1**} \\ \cline{2-5}  & **Purity** & **KL Divergence** & **Purity** & **KL Divergence** \\ \hline Unsupervised clustering & 0.794 & 0.142 & 0.794 & 0.142 \\ GRU network & 0.780 & 0.133 & 0.650 & 0.304 \\ GRU migration model & 0.916 & 0.042 & 0.785 & 0.067 \\ Label propagation algorithm & 0.909 & 0.022 & 0.821 & 0.077 \\ Our algorithm & 0.921 & 0.034 & 0.883 & 0.037 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experimental results of different algorithms under the condition of few labels. significantly improved. These two semi-supervised algorithms were regarded as enhancements to the clustering and directly trained classification models, which better synthesized the label dataset and target data to improve classification performance. However, the algorithm designed in this study achieved comparable or even better results than the two classical semi-supervised algorithms under the general low-label situation with five labeled data. The advantage was more obvious under the condition of extremely few labels. Therefore, based on the above analysis, it was proven that methods such as twinning network clustering and two-stage training designed in this study effectively utilized label data and were excellent semi-supervised classification algorithms. It was an excellent semi-supervised classification algorithm.