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

Complex Adaptive Systems Modeling

Table 2 Semi-supervised classification results of different algorithms on the USPS dataset

Method	P = 1		P = 2		P = 3
Method	Semi (%)	Test (%)	Semi (%)	Test (%)	Semi (%)	Test (%)
GFHF	72.39 ± 3.60	–	79.66 ± 3.67	–	83.39 ± 3.07	–
MFA	–	–	68.74 ± 3.82	66.52 ± 4.23	72.76 ± 4.21	70.57 ± 3.19
SDA	56.86 ± 3.11	54.91 ± 3.92	67.37 ± 3.26	67.43 ± 2.91	72.66 ± 2.64	69.32 ± 3.20
TCA	70.39 ± 3.38	65.36 ± 3.17	76.52 ± 3.21	71.27 ± 3.28	79.58 ± 3.37	72.76 ± 2.94
LapRLS	57.89 ± 4.08	58.42 ± 4.36	69.03 ± 3.86	69.39 ± 2.49	76.02 ± 3.28	74.08 ± 2.79
FME	74.75 ± 6.52	67.91 ± 5.04	79.64 ± 3.41	73.26 ± 3.19	82.15 ± 2.26	74.97 ± 2.72
NNSG	76.98 ± 3.80	68.92 ± 3.37	81.17 ± 2.59	76.85 ± 2.57	84.50 ± 2.13	76.38 ± 2.54
GESR-LR	78.49 ± 3.65	69.56 ± 3.18	83.61 ± 2.36	77.28 ± 2.29	86.07 ± 2.73	78.20 ± 2.17