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

Complex Adaptive Systems Modeling

Table 3 Semi-supervised classification results of different algorithms on the ISOLET5 dataset

Method	P = 1		P = 2		P = 3
Method	Semi (%)	Test (%)	Semi (%)	Test (%)	Semi (%)	Test (%)
GFHF	49.29 ± 2.15	–	56.26 ± 2.44	–	61.13 ± 2.14	–
MFA	–	–	61.19 ± 2.14	61.46 ± 2.89	65.52 ± 2.27	65.19 ± 2.36
SDA	52.01 ± 2.38	51.19 ± 2.54	61.31 ± 2.28	61.57 ± 2.35	67.55 ± 2.28	67.91 ± 2.06
TCA	49.19 ± 2.94	49.30 ± 2.13	59.77 ± 2.36	59.16 ± 2.42	64.72 ± 2.37	65.01 ± 2.38
LapRLS	51.71 ± 3.03	50.98 ± 2.84	61.63 ± 2.37	61.85 ± 2.21	65.19 ± 1.89	65.25 ± 2.05
FME	49.92 ± 2.40	50.17 ± 2.49	59.92 ± 2.45	59.88 ± 2.56	65.98 ± 1.64	66.13 ± 2.29
NNSG	53.39 ± 2.26	51.75 ± 2.37	62.84 ± 2.57	62.63 ± 2.26	67.33 ± 2.21	67.94 ± 2.15
GESR-LR	55.01 ± 2.25	52.26 ± 2.82	63.09 ± 2.12	63.13 ± 2.43	69.26 ± 2.24	70.03 ± 1.78