# A Two-Stage Semi-Supervised High Maneuvering Target Trajectory Data Classification Algorithm
###### Abstract  
Labeled data in insufficient amounts and missing categories are two observable features for high maneuvering target trajectory data. However, the existing research achievements are insufficient for solving these two problems simultaneously during data classification. This study proposed a two-stage semi-supervised trajectory data classification algorithm. By pre-training the autoencoder and combining it with the Siamese network, a two-stage joint training was formed, which enabled the model to deal with missing categories by clustering and maintaining the classification ability under the missing label categories. The experimental simulation results showed that the performance of this algorithm was better than the classical semi-supervised algorithm label propagation and transferred learning when the amount of various labeled data was as low as 1-5. The two-stage training model also had a good effect on the problem of missing categories. When 75% of the types were missing, the purity could still reach 82%, which was about eight percentage points higher than the directly trained network. When two problems appeared simultaneously, compared with the directly trained network, the performance improved by about three percentage points on average, and the purity was consistently higher than the clustering results. In summary, this algorithm was more tolerant of the problems of labeled datasets, so it was more practical. t  
Footnote †: journal: Medical  
However, training deep network models requires a large amount of high-quality labeled data, which is difficult to directly use in the upper condition [7]. Aiming at the problem of insufficient label data, existing algorithms have abundant research results. For example, [PERSON] et al. [8] addressed this problem using a transfer learning approach, training the model on a dataset similar to the base model. In [9], [PERSON] et al. made full use of unlabeled data to pre-train a feature extraction network and train a classifier on this basis. [PERSON] et al. [10] used data augmentation to solve the problem of too few labels and used GAN (generative adversarial network) to screen out the data with the same distribution as the labeled data and used the five-label data as new training data. The above methods can effectively reduce the impact of insufficient label data. However, for the problem of missing categories, the processing power of existing algorithms is insufficient. Most of the existing semi-supervised algorithms are finally trained models or classification models, which can only distinguish the labeled and trained categories. For unlabeled categories, the classification performance will drop significantly. Existing algorithms generally distinguish untrained classes through anomaly detection or separate the classification and clustering processes, resulting in poor classification performance. So, this is a research direction worthy of attention for strengthening the existing semi-supervised classification algorithms in the field of trajectory classification. In particular, the ability to handle the problem of missing categories in labeled datasets needs to be improved. Therefore, this work studied the semi-supervised trajectory classification problem under the conditions of insufficient label data and missing categories and proposed a two-stage semi-supervised high maneuvering target trajectory data classification algorithm. The algorithm designed a network structure combining the Siamese network and autoencoder and accordingly designed a two-stage model training method. The method first used unlabeled data to pre-train the clustering model to ensure the clustering ability of the model for most of the samples in the target domain. Then it used the data in the labeled dataset to fine-tune the model to improve the model's processing ability for labeled categories.