Research Article
Robust Structure Preserving Nonnegative Matrix Factorization for Dimensionality Reduction
Table 3
Clustering performance on local noise contaminated datasets. The best performances are highlighted in bold font.
(a) |
| Dataset | Accuracy (%) | means | PCA | NMF | GNMF | SPNMF | RSPNMF |
| FERET | 23.10 ± 1.14 | 26.12 ± 1.27 | 24.20 ± 1.03 | 25.05 ± 0.74 | 27.45 ± 5.22 | 33.06 ± 1.25 | ORL | 35.71 ± 2.74 | 46.99 ± 2.69 | 44.56 ± 1.59 | 37.78 ± 1.37 | 44.78 ± 1.7 | 54.01 ± 3.3 | Yale | 32.12 ± 2.38 | 37.43 ± 2.27 | 38.18 ± 2.18 | 32.45 ± 1.16 | 36.36 ± 1.24 | 44.42 ± 1.08 |
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(b) |
| Dataset | Normalized mutual information (%) | means | PCA | NMF | GNMF | SPNMF | RSPNMF |
| FERET | 49.2 ± 2.56 | 56.85 ± 1.54 | 54.33 ± 1.12 | 50.06 ± 2.51 | 58.26 ± 0.89 | 67.44 ± 1.73 | ORL | 53.78 ± 2.56 | 65.79 ± 1.54 | 64.57 ± 1.12 | 58.65 ± 2.51 | 64.26 ± 0.89 | 71.50 ± 1.73 | Yale | 35.73 ± 2.49 | 42.72 ± 2.30 | 43.77 ± 1.58 | 38.84 ± 0.98 | 42.72 ± 1.12 | 47.56 ± 1.19 |
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