Fixed Point Theory and Algorithms for Sciences and Engineering

Table 3 Classification accuracies (%) and elapsed times (s) for the sparsity learning methods based on D1, D2, D3, D4, D5, and D6 applied to the datasets in Table 1

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

#	D1		D2		D3		D4		D5		D6
#	acc.	time	acc.	time	acc.	time	acc.	time	acc.	time	acc.	time
1	77.84	0.210	82.75	0.207	83.92	0.298	82.47	0.210	83.33	0.213	83.78	0.229
2	95.52	0.180	89.76	0.206	94.44	0.287	93.57	0.206	91.81	0.208	94.15	0.254
3	27.86	0.202	51.17	0.206	64.32	0.280	56.76	0.212	59.11	0.209	64.06	0.237
4	76.45	0.187	71.03	0.200	71.58	0.312	71.32	0.213	71.01	0.212	71.86	0.267
5	39.00	9.383	54.00	9.365	46.16	9.697	51.5	9.525	66.16	9.584	68.66	10.190
6	49.90	0.795	51.35	0.822	50.20	1.068	50.8	0.805	49.65	0.849	50.00	0.974
7	43.49	0.222	43.08	0.225	43.60	0.352	44.39	0.223	42.49	0.229	43.48	0.298
8	63.33	0.607	74.66	0.600	84.66	0.780	77.33	0.621	78.66	0.613	81.33	0.690
9	25.01	0.615	39.24	0.612	56.54	0.722	16.79	0.625	23.28	0.629	56.54	0.694
10	62.47	0.592	69.50	0.603	91.55	0.823	88.71	0.630	94.53	0.616	91.65	0.717
11	29.28	0.841	32.14	0.829	40.94	1.150	40.08	0.835	37.49	0.843	43.86	1.006
12	22.38	1.221	25.62	1.205	45.80	1.617	31.02	1.234	33.95	1.246	49.02	1.469
13	50.95	1.497	41.47	1.507	72.25	2.182	67.44	1.527	53.03	1.527	76.66	1.937
14	64.78	2.304	34.18	2.322	66.33	3.319	74.17	2.356	37.78	2.358	66.40	3.079
15	32.06	11.604	46.01	11.585	67.63	13.472	62.63	11.620	55.46	11.671	66.20	13.259
Ave.	50.69	2.031	53.73	2.033	65.33	2.424	60.60	2.056	58.52	2.067	67.18	2.353

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