# Fusing Local and Global Information for One-Step Multi-View Subspace Clustering
## 5 Experiments
### Experiment Results and Analysis  
In the experiments, four popular metrics including accuracy (ACC), normalized mutual information (NMI), F-score and adjusted Rand index (ARI) are utilized to assess the clustering result. These evaluation metrics reflect different natures of the clustering results, thus providing a comprehensive analysis from multiple perspectives. For all four of the evaluation metrics, higher values indicate better results. The comparison results are shown in Tables 3-6. The best result is highlighted in red font, and the second best result is reported in blue font. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**NMIn (\%)** & **3 sources** & **100 leaves** & **BBC** & **Caltech101** & **COIL-20** & **NottingHill** & **Webbkb** & **Cornell** & **Wikipedia** \\ \hline FeatConcate & 56.53 & 87.64 & 60.63 & 56.18 & 79.15 & 86.66 & 66.18 & 19.02 & 54.04 \\ Co-reg\_c & 55.03 & 92.04 & 13.38 & 43.68 & 81.69 & 69.79 & 8.79 & 12.60 & 26.22 \\ Co-reg\_p & 50.85 & 90.04 & 6.59 & 43.59 & 82.17 & 67.73 & 18.91 & 11.57 & 7.54 \\ LMSC & 69.18 & 89.22 & 65.70 & 51.85 & 84.54 & 78.57 & 70.36 & 18.89 & 52.60 \\ FMR & 57.34 & 85.44 & 66.04 & 46.77 & 78.53 & 66.38 & 59.59 & 21.44 & 51.81 \\ MLRSC & 5.75 & 13.33 & 1.03 & 2.11 & 2.66 & 0.23 & 0.08 & 4.86 & 2.31 \\ RMMKMC & 40.23 & 0.00 & 54.38 & 63.16 & 79.06 & 75.28 & 63.81 & 27.99 & 55.09 \\ mPAC & 64.13 & 73.96 & 47.41 & 50.56 & 85.86 & 83.14 & 16.79 & 15.48 & 47.53 \\ LMVSC & 59.35 & 87.49 & 69.45 & 55.70 & 82.24 & 82.33 & 69.41 & 27.84 & 52.81 \\ PMSC & 48.75 & 64.02 & 4.19 & 21.38 & 70.31 & 65.73 & 7.95 & 10.42 & 6.47 \\ COMVSC & 50.69 & 87.19 & 57.67 & 52.70 & 87.50 & 75.16 & 23.73 & 24.51 & 53.58 \\ GMC & 53.73 & 95.37 & 55.62 & 53.77 & 96.31 & 9.23 & 0.17 & 15.90 & 29.98 \\ Ours & 77.74 & 97.43 & 75.20 & 54.23 & 97.44 & 100.00 & 84.84 & 39.40 & 56.06 \\ \hline \end{tabular}
\end{table} 
Table 4: The clustering performance comparison in terms of NMI on nine multi-view data sets. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**ACC (\%)** & **3 sources** & **100 leaves** & **BBC** & **Caltech101** & **COIL-20** & **NottingHill** & **Webbkb** & **Cornell** & **Wikipedia** \\ \hline FeatConcate & 65.09 & 71.00 & 61.46 & 54.27 & 67.50 & 91.93 & 94.77 & 43.08 & 57.72 \\ Co-reg\_c & 69.17 & 78.53 & 34.74 & 42.00 & 70.36 & 74.77 & 80.42 & 38.41 & 38.59 \\ Co-reg\_p & 66.18 & 75.60 & 35.99 & 42.14 & 72.42 & 72.14 & 83.24 & 36.26 & 20.70 \\ LMSC & 71.60 & 77.00 & 86.28 & 53.80 & 75.35 & 83.78 & 95.34 & 43.59 & 56.85 \\ FMR & 70.41 & 69.25 & 85.11 & 47.69 & 72.01 & 82.85 & 93.24 & 43.08 & 56.85 \\ MLRSSC & 34.88 & 1.44 & 33.14 & 54.21 & 5.07 & 30.11 & 78.02 & 43.08 & 15.22 \\ RMMKMC & 54.44 & 1.00 & 60.44 & 54.14 & 61.60 & 75.43 & 94.01 & 43.59 & 61.04 \\ mPAC & 76.92 & 47.06 & 58.10 & 59.36 & 73.40 & 90.28 & 78.12 & 45.64 & 56.71 \\ LMVSC & 63.31 & 71.06 & 84.38 & 56.72 & 74.17 & 89.25 & 95.62 & 55.90 & 59.16 \\ PMSC & 63.85 & 22.46 & 34.45 & 44.45 & 49.09 & 70.21 & 78.02 & 45.44 & 19.70 \\ COMVSC & 65.09 & 70.88 & 69.49 & 77.54 & 77.64 & 81.70 & 82.87 & 55.38 & 60.46 \\ GMC & 65.09 & 86.38 & 69.05 & 65.74 & 87.57 & 31.24 & 77.64 & 38.97 & 31.89 \\ Ours & 83.43 & 94.31 & 88.47 & 79.85 & 92.08 & 100.00 & 98.38 & 64.62 & 62.63 \\ \hline \end{tabular}
\end{table} 
Table 3: The clustering performance comparison in terms of ACC on nine multi-view data sets. **Experiment Analysis. ** Through observing the clustering results from Tables 3-6, one can see that our proposed method can obtain the best results on all multi-view data sets except Caltech101. For the Caltech101 data set, LGOMSC is lower than FeatConcate and RMKMC in terms of NMI. However, in terms of ACC, our proposed method exceeds the second best results on the data sets including 3 sources, 100 leaves, BBC, Caltech101, COIL-20, NottingHill, Webbk, Cornell, and Wikipedia by 6.51%, 7.93%, 2.19%, 2.31%, 4.51%, 8.07%, 2.76%, 8.72% and 1.59%, respectively. For NMI, our proposed method is 8.93% lower than the best method, RMKMC, on the Caltech101 data set.