# A Two-Stage Semi-Supervised High Maneuvering Target Trajectory Data Classification Algorithm
## 4 Experiment and Analysis
### Label Data Amount Insufficient Problem  
This section verifies the ability of the algorithm to deal with the problem of insufficient labels and does not consider the problem of unlabeled categories. The label data set used was consistent with the target data set trajectory category. It was generally considered that less than 10 labels for each category were the condition of few labels. Firstly, the influence of different amounts of labeled data on the algorithm of this study was studied, and the robustness of the algorithm to the problem of few labels was tested. The statistical results are shown in Table 2.  
As can be seen from the table, with the reduction of various label data, the overall clustering purity slowly decreased, and the KL divergence kept fluctuating at a low level, indicating that the overall effect of clustering was maintained at a high level. Even under the extreme condition that the number of labels for each type of data was 1, the performance could be significantly improved compared with the last unlabeled clustering model. It showed that the algorithm in this study could effectively utilize the label data and was robust in the reduction of label data. First, we observed the model loss curve in the algorithm training process, compared it with the general classification model training curve, and conducted experiments with five label data for each category. The results are shown in Figure 11.  
Figure 11 show the change of the loss curve during the training of the two algorithms on the same few-label dataset. Although it cannot be numerically compared due to the different models and loss functions, it can be clearly seen that during the training process of the model in this paper, the training set and test set losses were closer. It shows that the model in this paper uses the label data more reasonably, and the possibility of overfitting is lower. Then, the algorithm in this study was compared with other classic semi-supervised algorithms to test the ability of the algorithm to utilize label data. The comparison algorithm selected the label propagation algorithm [22] and the GRU migration model algorithm. The label propagation algorithm is a popular semi-supervised clustering method. The principle is to use the labeled data as the starting point, calculate the distance between samples, and judge the samples with closer distances as the corresponding category. In this paper, DTW [23] distance is used for label propagation. Compared with unsupervised clustering methods, label propagation algorithms can make better use of labeled data and significantly improve clustering accuracy. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**The Amount of** & **20** & **10** & **5** & **1** & **0** \\
**Various Labels** & & & & & **(Clustering)** \\ \hline Purity & 0.943 & 0.945 & 0.921 & 0.898 & 0.794 \\ KL divergence & 0.016 & 0.047 & 0.034 & 0.037 & 0.142 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental results of different training samples. Figure 10: PCA diagram. (**a**) PCA diagram before fine-tuning and (**b**) PCA diagram after fine-tuning. Another comparison algorithm referred to the algorithm principle in [9], which used unlabeled data to train a clustering model to provide auxiliary information and then transferred the model to a classification network for fine-tuning. Hereafter, this paper calls it the GRU migration model. The general idea of this model is similar to this study; the main difference lies in the Siamese neural network and the handling of the missing category problem. Comparing our algorithm with this algorithm, we could test the working effect of the Siamese neural network and the ability of the algorithm to utilize data information. In addition, the unsupervised clustering and the GRU classification model obtained by direct training were used as comparison algorithms.