# Consensus Guided Multi-View Unsupervised Feature Selection with Hybrid Regularization
## 4 Experiments
### Convergence Behavior Analysis  
To substantiate our algorithm's operational efficiency, we conduct systematic convergence analysis across three benchmark datasets: handwritten, \(MSRCV1\), and WebKB. Figure 5 demonstrates the monotonic descent pattern of the objective function during iterative optimization. Notably, the algorithm achieves convergence within a limited number of iterations, characterized by rapid decline of objective values (particularly in the initial five iterations) followed by asymptotic stabilization. This empirical evidence substantiates that \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{6}{c}{**Datasets**} \\ \cline{2-7}  & **MSRCV1** & **Yale** & **Handwritten** & **Outdoor Scene** & **ORL** & **WebKB** \\ \hline ASWW [14] & \(69.43\pm 6.12\) & \(44.00\pm 2.25\) & \(80.12\pm 7.05\) & \(47.56\pm 1.98\) & \(33.1\pm 1.54\) & \(56.11\pm 6.53\) \\ CGMV-FS [12] & \(68.14\pm 5.41\) & \(42.88\pm 3.13\) & \(67.66\pm 4.85\) & \(26.95\pm 0.65\) & \(33.49\pm 1.16\) & \(58.18\pm 5.98\) \\ CRV-DGL [34] & \(77.05\pm 7.79\) & \(50.18\pm 5.59\) & \(79.92\pm 6.75\) & \(61.15\pm 4.50\) & \(54.56\pm 3.37\) & \(73.37\pm 7.38\) \\ NSGL [35] & \(69.88\pm 4.89\) & \(39.82\pm 3.52\) & \(75.91\pm 4.85\) & \(45.77\pm 3.02\) & \(40.75\pm 2.62\) & \(72.02\pm 4.58\) \\ TLR [36] & \(81.19\pm 7.32\) & \(48.58\pm 4.82\) & \(81.74\pm 6.73\) & \(42.87\pm 3.31\) & \(55.25\pm 3.39\) & \(76.82\pm 1.93\) \\ CvLP-DGL [25] & \(73.57\pm 3.61\) & \(46.18\pm 4.53\) & \(73.05\pm 6.55\) & \(62.83\pm 3.97\) & \(58.89\pm 2.94\) & \(70.96\pm 7.91\) \\ CCSFS- [11] & \(78.36\pm 5.20\) & \(54.64\pm 4.30\) & \(84.28\pm 7.30\) & \(62.16\pm 3.50\) & \(58.36\pm 3.76\) & \(75.34\pm 8.06\) \\ CDMvFS [15] & \(82.46\pm 6.16\) & \(54.58\pm 5.85\) & \(86.78\pm 7.69\) & \(62.58\pm 6.00\) & \(60.18\pm 2.89\) & \(75.71\pm 8.86\) \\ Our & \(\mathbf{83.14\pm 0.79}\) & \(\mathbf{57.82\pm 1.85}\) & \(\mathbf{92.57\pm 0.63}\) & \(61.76\pm 0.66\) & \(\mathbf{62.80\pm 1.66}\) & \(70.15\pm 1.38\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: ACC% \(\pm\) std% of different methods on different datasets. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{6}{c}{**Datasets**} \\ \cline{2-7}  & **MSRCV1** & **Yale** & **Handwritten** & **Outdoor Scene** & **ORL** & **WebKB** \\ \hline ASWW [14] & \(61.29\pm 6.12\) & \(49.55\pm 1.72\) & \(78.24\pm 3.34\) & \(39.76\pm 1.01\) & \(55.59\pm 1.51\) & \(11.73\pm 3.99\) \\ CGMV-FS [12] & \(58.04\pm 3.80\) & \(48.59\pm 2.26\) & \(67.31\pm 2.54\) & \(11.82\pm 0.42\) & \(55.77\pm 0.96\) & \(13.92\pm 8.49\) \\ CRV-DGL [34] & \(68.61\pm 5.18\) & \(57.72\pm 4.82\) & \(77.31\pm 2.83\) & \(49.01\pm 1.46\) & \(73.76\pm 2.03\) & \(35.14\pm 9.62\) \\ NSGL [35] & \(61.29\pm 3.78\) & \(46.15\pm 2.80\) & \(72.77\pm 2.83\) & \(37.59\pm 0.66\) & \(62.48\pm 1.67\) & \(33.73\pm 2.36\) \\ TLR [36] & \(74.67\pm 4.79\) & \(53.01\pm 3.59\) & \(81.44\pm 3.69\) & \(37.66\pm 0.79\) & \(74.23\pm 1.44\) & \(39.71\pm 4.79\) \\ CvLP-DGL [25] & \(64.21\pm 4.03\) & \(50.05\pm 4.18\) & \(70.26\pm 3.33\) & \(49.85\pm 1.28\) & \(76.43\pm 1.82\) & \(35.96\pm 5.07\) \\ CCSFS [11] & \(70.61\pm 3.32\) & \(58.42\pm 3.42\) & \(79.77\pm 3.52\) & \(53.33\pm 0.56\) & \(76.09\pm 2.17\) & \(42.71\pm 10.32\) \\ CDMvFS [15] & \(72.66\pm 5.00\) & \(59.72\pm 4.29\) & \(82.69\pm 4.27\) & \(51.13\pm 2.11\) & \(77.89\pm 1.20\) & \(44.04\pm 10.52\) \\ Our & \(\mathbf{75.10\pm 1.28}\) & \(\mathbf{62.39\pm 2.43}\) & \(\mathbf{85.50\pm 1.51}\) & \(\mathbf{54.05\pm 0.60}\) & \(\mathbf{80.03\pm 0.67}\) & \(\mathbf{44.32\pm 1.27}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: NMI% \(\pm\) std% of different methods on different datasets. our method exhibits desirable computational efficiency while demonstrating scalability for processing large-scale datasets. Figure 3: The normalized mutual information (NMI) obtained by applying various feature selection methods on different datasets.