## 5 Experiments and Results
### Results and Analysis  
Each subfigure represents the results of the experiments, each involving a specific type of data corruption for a particular attribute within the datasets. The horizontal axis shows the levels of data corruption types, and the vertical axis illustrates the NMAE values of the four models in each case. For Gaussian and impulsive noise, Figures 8 and 9 illustrate the results of the comparison of our models and others on the NDBC-TAO and U.S. Climate Normal datasets. Across all models, the proposed model consistently outperformed the other methods. In particular, the NMAE values of our method applied to the two datasets consistently fell below 0.1 in most noise cases. Figure 8: \(Cont.\)  
Figure 8: Reconstruction of Gaussian noise cases. (**a**) Temperature attribute of NDBC-TAO dataset; (**b**) density attribute of NDBC-TAO dataset; (**c**) salinity attribute of NDBC-TAO dataset; (**d**) dew attribute of U.S. Climate Normal dataset; (**e**) temperature attribute of U.S. Climate Normal dataset; (**f**) wind attribute of U.S. Climate Normal dataset. Figure 9: Reconstruction of impulsive noise cases. (**a**) Temperature attribute of NDBC-TAO dataset; (**b**) density attribute of NDBC-TAO dataset; (**c**) salinity attribute of NDBC-TAO dataset; (**d**) dew attribute of U.S. Climate Normal dataset; (**e**) temperature attribute of U.S. Climate Normal dataset; (**f**) wind attribute of U.S. Climate Normal dataset. In addition, the proposed model can handle outliers. The NMAE values of all models in outlier cases are shown in Figure 10 for the two datasets. The results of the other models are worse than those of the proposed model, and the results of our model applied to the two datasets are below \(0.1\) in most outlier cases. Finally, the missing values are also reconstructed by our model, and the results of an experiment on the two datasets in the missing-value cases of the proposed model and the three different models are depicted in Figure 11. The experiment shows that the NMAE of the proposed model surpasses the NMAE values of the WTC, TRPCA, and TRPCA-TNN methods and maintains a value below 0.1 when applied to the two datasets in most missing-value cases. Figure 10: Reconstruction of outlier cases. (**a**) Temperature attribute of NDBC-TAO dataset; (**b**) density attribute of NDBC-TAO dataset; (**c**) salinity attribute of NDBC-TAO dataset; (**d**) dew attribute of U.S. Climate Normal dataset; (**e**) temperature attribute of U.S. Climate Normal dataset; (**f**) wind attribute of U.S. Climate Normal dataset. Although each figure shows a comparison of the methods for each type of corruption, the default datasets consistently include a blend of noise, outliers, and missing values. Therefore, the reason the best outcomes were achieved by the proposed model is that it considers all types of corruption by employing WTSVT as a weighted approach for the TRPCA and TC, which shrinks singular values based on their significance and improves the capacity to reduce mixture noise and outliers and reconstruct missing values. Every singular value has distinct importance, as indicated in Section 5.2, and the more significant the data expressed, the greater the singular value. Therefore, we must reduce the distinct singular values using varying weights rather than fixed weights, as in most studies. In contrast, the WTC method consistently underperforms in all corruption cases. The WTC method performs poorly in all cases because it specializes in reconstructing missing values and performs poorly in reducing noise and outliers. The TRPCA method consistently outperforms the WTC method. However, it still requires consideration of the weights of the singular values to enhance its ability to handle mixed noise and outliers. In some cases  
Figure 11: Reconstruction of missing value cases. (**a**) Temperature attribute of NDBC-TAO dataset; (**b**) density attribute of NDBC-TAO dataset; (**c**) salinity attribute of NDBC-TAO dataset; (**d**) dew attribute of U.S.