# Research and Application of Semi-Supervised Category Dictionary Model Based on Transfer Learning
## 3 Experiments and Results Analysis
### Results Analysis
#### 3.3.2 Results Analysis of Bearing Fault Diagnosis Data Experiment  
Four classifiers, 1-NN, SRC, TCA [9] and GFK [10], were selected as comparison benchmarks to validate the effectiveness of the SSDT model in bearing fault diagnosis. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Task** & **1-NN [25]** & **SRC [26]** & **LSDT [27]** & **TSC + LR [4]** & **SSDT** \\ \hline L\(\rightarrow\)V & 61.02 & 57.69 & 53.47 & 57.42 & **61.74** \\ \hline L\(\rightarrow\)S & **55.86** & 48.00 & 46.74 & 48.39 & 51.70 \\ \hline S\(\rightarrow\)L & 48.47 & 51.04 & 53.09 & 50.78 & **54.71** \\ \hline S\(\rightarrow\)V & 62.93 & 62.04 & 65.90 & 66.50 & **66.97** \\ \hline V\(\rightarrow\)L & 57.06 & 57.18 & **57.29** & 55.05 & 56.83 \\ \hline V\(\rightarrow\)S & 53.23 & 65.04 & **66.79** & 65.38 & 65.78 \\ \hline Average & 56.43 & 56.83 & 57.21 & 57.35 & **59.62** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Experimental output of each model, based on cross-domain dataset fc7 layer depth features (max %). (The best results for different tasks are bolded). Figure 8: Comparison of the average performance of each model, based on the cross-domain dataset fc7 layer depth feature. Figure 9: Comparison of the best performance of each model, based on cross-domain dataset fc7 layer depth features. In the case of the SRC model, the sparse regularization parameter was set to 0.1. The procedure for the SRC model, as outlined in the literature [28], was employed in the experiments. This involves the use of a fast l'1 optimization algorithm [29] to solve for the sparse representation coefficients. For the GFK model, set the subspace dimension \(d=350\). For the TCA model, set the subspace dimension \(d=200\) and the regularization parameter \(\mu=0.1\). The experimental results of the SSDT model in the bearing fault diagnosis process are presented in Table 5 and Figure 10. These results show that the SSDT model yields the highest correct classification rate (highlighted in bold) in terms of both average and maximum values. This represents a significant performance improvement in comparison to 1-NN, SRC, GFK, and TCA, suggesting that the SSDT model is also suitable for the bearing fault diagnosis task. An examination of the impact of iteration count on the performance of the SSDT model is presented via a variation curve of the accuracy rate with respect to the number of iterations in Figure 11. The observed positive correlation between the number of augmentation iterations and the correct classification rate suggests that incorporating a category dictionary-based reconstruction of residual sample augmentation operation contributes to enhancing the model's generalization performance. Comparative experiments involving the 1-NN model demonstrated considerable fluctuations in the output results, as indicated by the variance values. Despite the presence of a modest number of labeled samples in the target domain, the limited sample size, relying solely on existing labels in the target domain, generally precludes the classifier from achieving robust generalization ability. This suggests that the inclusion of source domain samples to aid in target domain classification tasks can effectively enhance the generalization performance of the classifier. Figure 10: Comparison of the performance of the models on the migration task in the field of bearing fault diagnosis. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Value** & **1-NN [25]** & **SRC [26]** & **GFK [10]** & **TCA [9]** & **SSDT** \\ \hline Average & 62.28 \(\pm\) 1.88 & 86.69 \(\pm\) 0.72 & 79.95 \(\pm\) 1.27 & 75.88 \(\pm\) 0.65 & **87.40 \(\pm\) 0.90** \\ \hline Maximum & 71.72 & 88.22 & 81.45 & 77.05 & **89.51** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of the experimental results. (The best results for different tasks are bolded). It is worth noting that the calculation time difference between different methods is not significant. In a regular configuration computer, the calculation time is usually a few seconds.