Fig 1.
Overview of the shared graph matrix construction.
Fig 2.
The examples of similarity matrices.
The elements of zero values are illustrated with white and those of non-zero values are illustrated with blue.
Fig 3.
Simulated dataset example.
Table 1.
Detailed information on the simulated dataset.
Table 2.
Detailed information on the datasets that are used for clustering.
Fig 4.
NMI results of various algorithms.
Table 3.
Best clustering results (ACC±std) of various algorithms.
Fig 5.
Performance of LRGO-MVDR under various parameter values for clustering tasks on four datasets.
Fig 6.
Convergence curves of LRGO-MVDR on four datasets for clustering.
Table 4.
Best clustering results (ACC±std) of three methods for solving Eq (19).
Table 5.
Detailed information on the datasets for classification.
Fig 7.
Classification performance of various algorithms.
Table 6.
Best classification results (HVA±std) of various algorithms.
Fig 8.
Performance of LRGO-MVDR under various parameter values for classification tasks on four datasets.
Fig 9.
Convergence curves of LRGO-MVDR on four datasets for classification.
Table 7.
Best classification results (HVA±std) of three methods for solving Eq (19).
Table 8.
p-values of the Wilcoxon rank sum tests on clustering tasks (ACC).
Table 9.
p-values of the Wilcoxon rank sum tests on clustering tasks (NMI).
Table 10.
p-values of the Wilcoxon rank sum tests on classification tasks (HVA).