# Research and Application of Semi-Supervised Category Dictionary Model Based on Transfer Learning
## 4 Conclusions  
For cross-domain transfer tasks involving a limited number of labeled samples within the target domain, a semi-supervised category dictionary augmentation transfer learning model is proposed. This study presents a semi-supervised category dictionary augmentation transfer learning model, developed on the foundation of existing category dictionary sparse representation classification and dictionary transfer methods. This model is based on TSC and augmented dictionary domain adaptation, performing confident sample selection in each iteration using category reconstruction error as the criterion.  
This study presents a semi-supervised category dictionary augmentation transfer learning model, developed on the foundation of existing category dictionary sparse representation classification and dictionary transfer methods. By employing the \(\ell^{1}\) parameter as the sparse representation constraint, the model unifies the conditional probability distribution and intra-class sample distance to construct the total objective function. This model, compared to the transfer sparse coding (TSC) approach, does not necessitate the additional design of classifiers, and is adept at leveraging labeled sample class information.  
To further bridge the gap between different domains, residuals are reconstructed based on unlabeled target domain samples on the category representation dictionary to procure sample labels. Moreover, a specific number of target domain samples are chosen in each iteration, based on the normalized discrete probability distribution in each class of pseudo-labeled samples. These samples are then amalgamated with source domain samples to generate an augmented data matrix. With the use of a step-by-step solution strategy, the category dictionary is established alongside the sparse representation coefficient update formula to attain an optimal solution.  
The applicability of the proposed model is evaluated through its implementation in a real-world bearing fault diagnosis. For comparative analysis, migration and non-migration models such as 1-NN and SRC serve as benchmarks to gauge the classification effectiveness of each model. Based on the evaluation results, the proposed model demonstrated superior performance compared to the benchmark models, achieving higher average correct classification rates and maximum values.  
Figure 11: Variation curve of model performance with the number of iterations.  
**Author Contributions:** Conceptualization, Y.D. and H.Y.; methodology, Y.D. and B.Z.; software, Y.L. and H.S.; validation, H.S. and B.Z.; investigation, H.Y.; data curation, B.H.; writing--original draft preparation, Y.L.; writing--review and editing, H.S.; supervision, Y.D. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Science and Technology project of State Grid Corporation: Research and application of insulator string digitization for UHV transmission line based on multi-parameter composite perception, grant number 520950220005.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The experimental data employed in this study were procured from the Bearing Data Center at Case Western Reserve University. Bearing test data. Case Western Reserve University Bearing Data Center [30].  
**Acknowledgments:** [PERSON] and [PERSON] are corresponding authors. This research was supported by Science and Technology project of State Grid Corporation: Research and application of insulator string digitization for UHV transmission line based on multi-parameter composite perception (520950220005).  
**Conflicts of Interest:** The authors declare no conflict of interest.