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

Complex Adaptive Systems Modeling

Table 4 Semi-supervised classification results of different algorithms on the ORL dataset

Method	P = 1		P = 2		P = 3
Method	Semi (%)	Test (%)	Semi (%)	Test (%)	Semi (%)	Test (%)
GFHF	52.81 ± 4.31	–	63.26 ± 3.78	–	68.97 ± 3.54	–
MFA	–	–	78.22 ± 4.25	79.11 ± 3.76	85.40 ± 3.89	84.78 ± 2.54
SDA	65.29 ± 2.72	65.32 ± 2.83	75.84 ± 3.61	76.92 ± 3.25	82.44 ± 2.54	82.95 ± 2.26
TCA	64.75 ± 2.05	64.61 ± 2.29	77.02 ± 3.15	78.80 ± 2.57	84.49 ± 3.12	84.27 ± 2.67
LapRLS	61.49 ± 3.31	59.88 ± 3.10	78.29 ± 2.54	77.86 ± 2.71	85.83 ± 2.75	85.94 ± 2.39
FME	68.25 ± 2.58	66.69 ± 3.24	80.80 ± 3.25	80.73 ± 2.76	85.92 ± 3.67	84.35 ± 2.64
NNSG	71.86 ± 3.29	67.77 ± 3.73	82.57 ± 2.65	82.91 ± 2.15	86.38 ± 3.83	85.52 ± 2.97
GESR-LR	73.08 ± 3.17	69.29 ± 3.68	85.52 ± 2.14	85.64 ± 2.89	87.45 ± 3.54	86.12 ± 2.99