Table 2.
Classification accuracies of proposed and baseline methods.
| Model | SCa | FCb | SC + FC |
|---|---|---|---|
| Decision Tree | 54.35% | 60.87% | 60.87% |
| Naïve Bayes | 56.52% | 60.87% | 71.74% |
| k-NN | 54.35% | 71.74% | 54.35% |
| SVM | 63.04% | 60.87% | 63.04% |
| Chi2 test | 67.39% | 71.74% | 67.39% |
| PCC | 67.39% | 71.74% | 71.74% |
| MIC | 67.39% | 69.57% | 67.39% |
| RFE | 69.57% | 71.74% | 71.74% |
| PCA | 67.39% | 71.74% | 73.91% |
| PCA + RFE | 67.39% | 71.74% | 76.09% |
| Naïve NMF | 63.04% | 73.91% | 76.09% |
| SSNMF | 69.57% | 73.91% | 78.26% |
| Convex NMF | 65.22% | 73.91% | 78.26% |
| SCNMF | 69.57% | 73.91% | 82.61% |
a
SC is structural connectivity.
b
FC is functional connectivity. This table reports the best performances of the proposed and baseline methods with tuned parameters. SCNMF achieved such performances with the latent dimensionality as 15, 39, 9 for SC, FC and multi-modal case, respectively.