# Tensioned Multi-View Ordered Kernel Subspace Clustering
## 4 Experiments
### Experiments on Multi-View News Datasets  
We evaluate the performance of TMOKSC with other subspace clustering algorithms on Multi-View News Datasets [29]; each view has 2000 dimensions. The documents from Reuters are presented in English, Spanish, French, German and Italian. In our experiment, we randomly selected a subset of 1200 documents (seen from Tables 6 and 7), which were divided into six categories, and the parameters in the formula were set as follows: \(\alpha=1{\times}10^{-2}\), \(\beta=1{\times}10^{-5}\).  
The results of the experiment were evaluated both in NMI (%) and ACC (%) measures. The reason for the excellent performance of our TMOKSC method might be that it emphasized the inherent context characteristics of the data in document clustering. Therefore, the final affinity matrix could be strengthened with special weights made up for the characteristics of multi-view data. The sequential kernel representation of The TMOKSC algorithm  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reuters** & **WMSC** & **AASC** & **AWP** & **TIMKSC** & **TMOKSC** \\
**Missing Rate** & **WMSC** & **AASC** & **AWP** & **TIMKSC** & **TMOKSC** \\ \hline
0.1 & 20.42\({}_{\pm 0.00}\) & 19.58\({}_{\pm 0.00}\) & 20.57\({}_{\pm 0.63}\) & 98.42\({}_{\pm 0.00}\) & 99.42\({}_{\pm 0.00}\) \\
0.2 & 20.22\({}_{\pm 0.04}\) & 19.69\({}_{\pm 0.15}\) & 20.96\({}_{\pm 0.51}\) & 98.50\({}_{\pm 0.00}\) & 99.67\({}_{\pm 0.00}\) \\
0.3 & 24.50\({}_{\pm 0.20}\) & 25.59\({}_{\pm 0.02}\) & 21.85\({}_{\pm 0.18}\) & 98.42\({}_{\pm 0.00}\) & 99.50\({}_{\pm 0.00}\) \\
0.4 & 21.09\({}_{\pm 0.20}\) & 23.52\({}_{\pm 0.11}\) & 19.48\({}_{\pm 0.74}\) & 98.08\({}_{\pm 0.00}\) & 99.17\({}_{\pm 0.00}\) \\
0.5 & 17.32\({}_{\pm 0.13}\) & 19.82\({}_{\pm 0.05}\) & 18.31\({}_{\pm 0.22}\) & 98.58\({}_{\pm 0.00}\) & 98.58\({}_{\pm 0.00}\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: The accuracy (%) scores over 20 runs by different approaches on the Reuters dataset.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Renters** & **WMSC** & **AASC** & **AWP** & **TIMKSC** & **TMOKSC** \\
**Missing Rate** & **WMSC** & **AASC** & **AWP** & **TIMKSC** & **TMOKSC** \\ \hline
0.1 & 8.11\({}_{\pm 0.00}\) & 6.33\({}_{\pm 0.02}\) & 7.54\({}_{\pm 0.70}\) & 95.64\({}_{\pm 0.00}\) & 98.10\({}_{\pm 0.00}\) \\
0.2 & 6.81\({}_{\pm 0.04}\) & 6.54\({}_{\pm 0.45}\) & 7.60\({}_{\pm 0.30}\) & 95.84\({}_{\pm 0.00}\) & 98.98\({}_{\pm 0.00}\) \\
0.3 & 7.06\({}_{\pm 0.09}\) & 7.52\({}_{\pm 0.01}\) & 6.82\({}_{\pm 0.11}\) & 95.86\({}_{\pm 0.00}\) & 98.37\({}_{\pm 0.00}\) \\
0.4 & 5.18\({}_{\pm 0.32}\) & 7.02\({}_{\pm 0.17}\) & 3.33\({}_{\pm 0.70}\) & 95.03\({}_{\pm 0.00}\) & 97.57\({}_{\pm 0.00}\) \\
0.5 & 1.25\({}_{\pm 0.14}\) & 2.07\({}_{\pm 0.02}\) & 2.62\({}_{\pm 0.21}\) & 96.05\({}_{\pm 0.00}\) & 96.10\({}_{\pm 0.00}\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: The NMI scores (%) over 20 runs by different approaches on the Reuters dataset.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**ORL** & **WMSC** & **AASC** & **AWP** & **DSC** & **TIMKSC** & **TMOKSC** \\
**Missing Rate** & **WMSC** & **AASC** & **AWP** & **DSC** & **TIMKSC** & **TMOKSC** \\ \hline
0.1 & 80.10\({}_{\pm 1.63}\) & 67.26\({}_{\pm 2.33}\) & 73.39\({}_{\pm 3.97}\) & 70 & 95.68\({}_{\pm 1.94}\) & 96.34\({}_{\pm 1.49}\) \\
0.2 & 72.08\({}_{\pm 2.12}\) & 60.75\({}_{\pm 2.31}\) & 68.94\({}_{\pm 2.14}\) & 58.50 & 95.95\({}_{\pm 1.69}\) & 96.59\({}_{\pm 1.85}\) \\
0.3 & 59.64\({}_{\pm 1.44}\) & 47.04\({}_{\pm 1.90}\) & 48.66\({}_{\pm 2.09}\) & 53.25 & 95.18\({}_{\pm 1.26}\) & 95.81\({}_{\pm 0.92}\) \\can improve the subspace learning ability of local views by tensor fusion of multi-view information and enhance complementarities between different views.