# A Two-Stage Semi-Supervised High Maneuvering Target Trajectory Data Classification Algorithm
## 2 Problem Analysis  
In order to more intuitively observe the impact of the insufficient amount and missing categories problems of the label datasets, we plotted the loss and classification accuracy of a conventional LSTM [11] classification network during training under the corresponding conditions. In the experiment, there are 12 types of target data. In order to facilitate the training of the model, it is assumed that there were 12 known types of classification targets. In the case of sufficient label data, 50 pieces of training data were taken for each type, and in the case of insufficient label data, 5 pieces of each class were taken as samples. Under the condition of missing categories, two types of label data were deleted. After one hot encoding of the labels, the calculation of the loss used the Categorical_Crossentropy function. Categorical_Crossentropy generally cooperated with Softmax for single-label classification, which was suitable for the classification of track types. The calculation formula is as follows:  
\[\text{Loss}=-\sum_{i=1}^{\text{output}}y_{i}\cdot\log\hat{g}_{i} \tag{1}\]The formula for calculating the accuracy is as follows:  
\[precision=\frac{TP}{TP+FP} \tag{2}\]  
In the formula, \(TP\) represents the number correctly marked and \(FP\) represents the number wrongly marked. The experimental results are shown in Figure 1.  
The two pictures in Figure 1a are the training curves under the insufficient label data amount condition. Due to the insufficient number of training samples, the curve significantly oscillated. The final training set accuracy stabilized at around 0.8, while the test set accuracy was about ten percentage points lower. Figure 1: Accuracy and loss curves in classification network training. (**a1**) Accuracy curve under the insufficient amount condition; (**a2**) loss curves under the insufficient amount condition; (**b1**) accuracy curve under the categories missing condition; (**b2**) loss curve under the categories missing condition; (**c1**) accuracy curve under the insufficient amount and categories missing condition; (**c2**) loss curve under the insufficient amount and categories missing condition. The two pictures in Figure 1b are the training curves under that the label data category missing condition. The training set loss function and accuracy could quickly tend to be optimal, and the training set classification accuracy even reached 100%. However, due to the existence of unlabeled categories in the test set, the accuracy of the test set significantly dropped to only about 60%. Under the condition that the two above problems existed at the same time, not only was the training set's effect optimal state difficult to achieve, but also the gap between the effect of the test set and training set was more obvious. In addition, the above training curve was obtained by training the model and drawing under the condition that the actual number of categories was known by default. In practical applications, when there were missing categories in the labeled data, the classification model could not distinguish the untrained categories. The negative effects of unknown classes in the target dataset were more severe than in Figure 1.  
To sum up, under semi-supervised conditions, the insufficient amount and missing categories problems of label datasets had a serious impact on the training of classification models. This study dealt with the trajectory classification problem with the above problems, so the algorithm required the ability to reduce the scale of the labeled data that the classification model relied on and the ability to deal with missing categories. First of all, for the problem of insufficient amount of label data, the solution in this study was to introduce a network structure called a Siamese neural network [12] and make full use of unlabeled data to provide auxiliary information in model training. A Siamese neural network is a network commonly used in metric learning and semi-supervised problems.