Combining graph embedding and sparse regression with structure low-rank representation for semi-supervised learning

Complex Adaptive Systems Modeling

Table 5 Semi-supervised classification results of different algorithms on the extended Yale B dataset

Method	P = 5		P = 10		P = 15
Method	Semi (%)	Test (%)	Semi (%)	Test (%)	Semi (%)	Test (%)
GFHF	27.49 ± 1.27	–	34.76 ± 2.11	–	40.13 ± 2.02	–
MFA	–	–	69.52 ± 3.19	70.08 ± 3.26	73.90 ± 2.72	74.15 ± 3.42
SDA	51.92 ± 2.36	52.06 ± 1.58	66.76 ± 1.65	67.49 ± 1.41	73.40 ± 1.19	73.08 ± 1.78
TCA	51.47 ± 2.19	52.56 ± 2.34	65.94 ± 1.95	66.76 ± 2.25	74.38 ± 1.76	74.28 ± 2.37
LapRLS	60.16 ± 2.24	59.47 ± 1.83	74.85 ± 1.67	74.19 ± 1.47	78.64 ± 2.54	78.08 ± 2.67
FME	63.46 ± 2.14	63.75 ± 1.89	76.92 ± 2.38	74.37 ± 1.22	80.38 ± 1.77	78.19 ± 2.03
NNSG	72.37 ± 2.25	66.92 ± 1.64	82.25 ± 1.64	75.42 ± 1.27	83.38 ± 1.93	79.06 ± 1.25
GESR-LR	75.26 ± 2.59	68.13 ± 1.54	84.11 ± 1.57	76.61 ± 1.95	85.87 ± 1.69	80.52 ± 1.28