# Tensioned Multi-View Ordered Kernel Subspace Clustering
## 5 Discussion of Results  
In this section, we observe that once the original instance of the Multi-View Datasets is lost, it is set to the zero vector, resulting in the failure of traditional methods. DSCN performs well relying on the pre-trained model when only a few instances in this dataset are unavailable. However, when the views are severely damaged, it cannot maintain consistent good performance. This indicates that the absence of views leads to severe information loss, exacerbating the information imbalance among different views. The tensor structure is conducive to the mining of consistent information to perform well. Especially the TMOKSC method that combines the tensor model with ordered kernel learning. The TMOKSC method demonstrates high efficiency on various datasets. Then, we delve into the parameter effects on clustering performance. We take all samples as information monitoring information on ORL datasets. Furthermore, we change the value of alpha and beta, respectively, with the other parameters fixed; Figure 2 displays the average clustering ACC value, NMI value, F_score value and CR value of our proposed TMOKSC methods as the missing rate rise from 0.1 to 0.5. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**COIL 20** & \multirow{2}{*}{**WMSC**} & **AASC** & **AWP** & **DSCN** & **TIMKSC** & **TMOKSC** \\
**Missing Rate** & & & & & & \\ \hline
0.1 & 20.67\({}_{\pm 0.00}\) & 19.15\({}_{\pm 0.00}\) & 20.61\({}_{\pm 0.55}\) & 73.40 & 84.51\({}_{\pm 0.00}\) & 85.24\({}_{\pm 0.12}\) \\
0.2 & 20.17\({}_{\pm 0.00}\) & 20.27\({}_{\pm 0.68}\) & 21.82\({}_{\pm 0.54}\) & 66.74 & 86.88\({}_{\pm 0.68}\) & 83.92\({}_{\pm 0.63}\) \\
0.3 & 21.60\({}_{\pm 0.71}\) & 20.00\({}_{\pm 0.00}\) & 21.78\({}_{\pm 0.24}\) & 49.38 & 88.93\({}_{\pm 0.15}\) & 94.01\({}_{\pm 0.19}\) \\
0.4 & 21.12\({}_{\pm 0.21}\) & 21.34\({}_{\pm 0.20}\) & 19.95\({}_{\pm 0.45}\) & 45 & 92.02\({}_{\pm 0.18}\) & 99.46\({}_{\pm 0.19}\) \\
0.5 & 18.13\({}_{\pm 0.34}\) & 20.26\({}_{\pm 0.27}\) & 18.40\({}_{\pm 0.27}\) & 32.92 & 96.67\({}_{\pm 0.00}\) & 98.42\({}_{\pm 0.06}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The accuracy (%) scores over 20 runs by different approaches on the COIL 20 dataset. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**COIL 20** & \multirow{2}{*}{**WMSC**} & **AASC** & **AWP** & **DSCN** & **TIMKSC** & **TMOKSC** \\
**Missing Rate** & & & & & & \\ \hline
0.1 & 8.03\({}_{\pm 0.00}\) & 6.32\({}_{\pm 0.00}\) & 8.20\({}_{\pm 0.44}\) & 81.99 & 90.97\({}_{\pm 0.00}\) & 91.80\({}_{\pm 0.12}\) \\
0.2 & 7.85\({}_{\pm 0.00}\) & 7.11\({}_{\pm 0.85}\) & 7.69\({}_{\pm 0.47}\) & 73.72 & 92.01\({}_{\pm 0.27}\) & 91.87\({}_{\pm 0.09}\) \\
0.3 & 7.15\({}_{\pm 0.15}\) & 6.24\({}_{\pm 0.00}\) & 6.19\({}_{\pm 0.23}\) & 62,77 & 92.43\({}_{\pm 0.12}\) & 96.36\({}_{\pm 0.19}\) \\
0.4 & 4.22\({}_{\pm 0.20}\) & 3.66\({}_{\pm 0.13}\) & 4.12\({}_{\pm 0.35}\) & 55.39 & 93.84\({}_{\pm 0.20}\) & 99.16\({}_{\pm 0.17}\) \\
0.5 & 2.11\({}_{\pm 0.07}\) & 2.66\({}_{\pm 0.13}\) & 2.52\({}_{\pm 0.27}\) & 44.72 & 97.08\({}_{\pm 0.00}\) & 98.32\({}_{\pm 0.08}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 9: The NMI scores (%) over 20 runs by different approaches on the dataset. We can draw conclusions from the Figure 3 that the clustering performance of each data set is relatively steady in the test parameter range in \(\alpha\) : 1\(\times\)10\({}^{-2}\)\(\sim\) 1\(\times\)10\({}^{3}\), \(\beta\) : 1\(\times\)10\({}^{-9}\)\(\sim\) 1\(\times\)10\({}^{-4}\). In most cases, The TMOKSC algorithm will cluster with consistent results. A quantitative analysis was conducted on the stability of the TMOKSC's clustering accuracy. When \(\alpha\) changed, the mean of ACC was 0.9329, the standard variance of ACC was 0.0209, the mean NMI was 0.9818, and the standard variance of NMI was 0.0052, indicating that the overall accuracy rate of the model fluctuated less and had good stability. When \(\beta\) changed, the mean value of ACC was 0.964, and the mean value of NMI was 0.9925.