Fixed Point Theory and Algorithms for Sciences and Engineering

Table 2 Classification accuracies (%) and elapsed times (s) for the sparsity learning methods based on SG, C1, C2, C3, and C4 applied to the datasets in Table 1

From: Stochastic approximation method using diagonal positive-definite matrices for convex optimization with fixed point constraints

#	SG		C1		C2		C3		C4
#	acc.	time	acc.	time	acc.	time	acc.	time	acc.	time
1	80.59	0.531	81.74	0.206	83.34	0.197	84.20	0.208	83.92	0.213
2	95.52	0.499	94.45	0.203	94.01	0.206	94.44	0.205	93.43	0.209
3	65.10	0.510	64.06	0.205	63.15	0.205	63.41	0.210	63.67	0.211
4	71.29	0.433	74.78	0.206	71.03	0.209	72.14	0.210	71.03	0.212
5	75.16	39.848	48.16	9.405	68.66	9.402	57.83	9.364	75.16	9.246
6	50.00	4.107	48.65	0.801	50.05	0.815	50.30	0.819	49.95	0.805
7	45.70	0.697	46.68	0.220	43.89	0.219	43.89	0.224	42.19	0.230
8	87.33	0.916	83.33	0.601	81.33	0.599	82.66	0.605	82.00	0.615
9	56.54	1.080	56.54	0.605	56.54	0.616	40.63	0.632	13.53	0.626
10	96.72	1.015	96.72	1.015	89.91	0.613	92.13	0.621	91.09	0.625
11	45.89	2.236	48.05	0.806	44.44	0.829	42.68	0.850	43.97	0.856
12	42.17	2.111	46.77	1.211	46.22	1.201	46.67	1.238	45.43	1.226
13	68.05	7.326	75.06	1.517	72.94	1.500	72.98	1.521	71.60	1.532
14	70.24	10.197	66.78	2.298	65.58	2.278	75.62	2.358	40.73	2.303
15	60.91	95.861	64.99	11.571	71.20	11.594	58.69	11.604	69.95	11.611
Ave.	67.41	11.158	66.04	2.030	66.82	2.032	65.22	2.045	62.51	2.035

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