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

Complex Adaptive Systems Modeling

Table 1 Semi-supervised classification results of different algorithms on the COIL-20 dataset

Method	P = 1		P = 2		P = 3
Method	Semi (%)	Test (%)	Semi (%)	Test (%)	Semi (%)	Test (%)
GFHF	78.65 ± 2.07	–	81.32 ± 1.77	–	84.56 ± 2.02	–
MFA	–	–	69.87 ± 2.24	70.10 ± 2.52	76.54 ± 2.28	76.27 ± 2.37
SDA	64.92 ± 2.07	65.80 ± 2.54	72.24 ± 2.19	73.19 ± 2.15	78.89 ± 2.05	78.19 ± 2.66
TCA	71.08 ± 2.23	70.83 ± 2.51	78.17 ± 3.15	77.29 ± 2.18	81.15 ± 2.32	80.96 ± 2.27
LapRLS	69.46 ± 2.58	69.73 ± 2.76	75.21 ± 2.66	75.16 ± 2.31	79.61 ± 2.54	79.85 ± 2.59
FME	76.31 ± 2.09	74.46 ± 2.13	82.35 ± 2.18	79.14 ± 2.39	85.86 ± 1.92	84.70 ± 2.03
NNSG	79.15 ± 2.86	75.31 ± 2.01	83.79 ± 2.69	80.88 ± 2.43	86.62 ± 2.29	82.13 ± 2.24
GESR-LR	81.09 ± 2.33	76.79 ± 2.18	85.29 ± 2.62	81.07 ± 2.59	87.12 ± 2.15	83.32 ± 2.16