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

Table 2 Comparison of clustering performance in multi-view datasets

Datasets	PAAD_HNSC_CHOL_GE		PAAD_ESCA_CHOL_GE		PAAD_HNSC_ESCA_GE		HNSC_ESCA_CHOL_GE
Datasets	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
NMFL_2,1	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

ISSN: 1479-7364