Deep Network Based on Stacked Orthogonal Convex Incremental ELM Autoencoders

Table 2

The comparisons of training and testing on the regression cases.

Datasets	Approaches	RMSE (training & testing)			⁢Hidden nodes & average time
		Nodes (fixed)	Training	Testing	# nodes	Time (s)
Auto MPG (0.08)	CI-ELM	20	0.1043	0.1035	66.29	0.1485
	PC-ELM	20	0.1014	0.1012	34.07	0.2783
	LOO-IELM	20	0.1106	0.1104	49.91	0.3183
	SB-ELM	20	0.1376	0.2307	≈130	0.0717
	II-RELM	20	0.0998	0.1005	44.17	0.3483
	EIR-ELM	20	0.0893	0.1005	31.05	0.3283
	OCI-ELM	20	0.0827	0.0823	23.62	0.2204
California Housing (0.12)	CI-ELM	150	0.1601	0.1583	330.09	1.0051
	PC-ELM	150	0.1389	0.1377	199.34	0.9810
	LOO-IELM	150	0.1376	0.1374	217.08	0.9766
	SB-ELM	150	0.1363	0.1369	—	—
	II-RELM	150	0.1341	0.1339	192.33	0.9713
	EIR-ELM	150	0.1274	0.1268	184.67	1.0017
	OCI-ELM	150	0.1272	0.1263	172.15	0.9704
Servo (0.115)	CI-ELM	100	0.1428	0.1419	182.63	0.0806
	PC-ELM	100	0.1373	0.1364	160.82	0.0701
	LOO-IELM	100	0.1371	0.1368	155.72	0.0765
	SB-ELM	100	0.1257	0.1254	127	0.0355
	II-RELM	100	0.1303	0.1307	157.12	0.0886
	EIR-ELM	100	0.1265	0.1264	147.80	0.0794
	OCI-ELM	100	0.1238	0.1232	143.56	0.0828
CCS (0.035)	CI-ELM	150	0.0611	0.0602	229.86	0.5893
	PC-ELM	150	0.0381	0.0365	162.79	1.1236
	LOO-IELM	150	0.0372	0.0369	159.04	0.9427
	SB-ELM	150	0.0366	0.0368	≈170	0.0872
	II-RELM	150	0.0361	0.0363	163.82	0.8341
	EIR-ELM	150	0.0348	0.0351	145.78	0.6305
	OCI-ELM	150	0.0332	0.0346	130.04	0.5835
Parkinsons (0.14)	CI-ELM	250	0.0913	0.0906	170.02	3.3403
	PC-ELM	250	0.0471	0.0463	63.55	4.7503
	LOO-IELM	250	0.0453	0.0462	59.78	4.4452
	SB-ELM	250	0.0388	0.0391	—	—
	II-RELM	250	0.0389	0.0383	77.19	4.4189
	EIR-ELM	250	0.0344	0.0347	48.92	3.9836
	OCI-ELM	250	0.0301	0.0283	39.92	3.0819