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Table 2 Comparison of clustering performance in multi-view datasets

From: Robust hypergraph regularized non-negative matrix factorization for sample clustering and feature selection in multi-view gene expression data

Datasets PAAD_HNSC_CHOL_GE PAAD_ESCA_CHOL_GE PAAD_HNSC_ESCA_GE HNSC_ESCA_CHOL_GE
AC (%) NMI (%) AC (%) NMI (%) AC (%) NMI (%) AC (%) NMI (%)
K-means 57.19 ± 0.21 20.71 ± 0.74 52.24 ± 0.33 6.67 ± 0.48 46.79 ± 0.07 14.35 ± 0.30 54.62 ± 0.09 15.93 ± 0.10
PCA 57.71 ± 0.02 18.38 ± 0.32 47.02 ± 0.12 1.00 ± 0.01 46.98 ± 0.08 13.63 ± 0.32 48.95 ± 0.04 10.70 ± 0.06
NMF 48.28 ± 0.28 15.95 ± 0.08 52.56 ± 0.17 6.05 ± 0.15 46.41 ± 0.00 13.27 ± 0.02 48.87 ± 0.14 9.74 ± 0.09
GNMF 53.46 ± 0.24 17.23 ± 0.37 47.68 ± 0.01 1.52 ± 0.01 44.82 ± 0.10 14.18 ± 0.28 52.95 ± 0.09 15.29 ± 0.10
NMFL2,1 58.69 ± 0.00 26.19 ± 0.00 57.17 ± 0.09 21.58 ± 0.03 50.21 ± 0.14 22.38 ± 0.26 51.70 ± 0.18 15.62 ± 0.09
HNMF 65.70 ± 0.02 32.18 ± 0.19 51.36 ± 0.07 25.64 ± 0.02 64.63 ± 0.08 26.90 ± 0.15 58.63 ± 0.09 19.32 ± 0.05
SHNMF 66.40 ± 0.03 35.62 ± 0.31 52.10 ± 0.07 26.01 ± 0.01 63.85 ± 0.04 36.93 ± 0.01 58.96 ± 0.06 19.07 ± 0.04
RGNMF 79.33 ± 0.83 60.42 ± 0.19 75.44 ± 0.76 60.52 ± 0.69 79.98 ± 0.81 53.74 ± 1.25 72.49 ± 1.35 38.36 ± 1.17
RHNMF 82.34 ± 0.71 62.26 ± 0.34 77.04 ± 0.65 63.96 ± 0.20 85.23 ± 0.62 60.05 ± 1.19 84.29 ± 0.98 52.72 ± 1.19
  1. Note: The best experimental results are highlighted in italics