TABLE 2.

Scenario 1: all four networks contribute to separation of classes within each dataset and association between the three views of data. Scenario 2: two networks contribute to both separation and association. FNSLDA (Ens) applies fused sparse LDA on separate views and perform classification on the combined discriminant vectors. FNSLDA (Stack) applies fused sparse LDA on stacked views. TPR-1; true positive rate for X¹. Similar for TPR-2 and TPR-3. FPR; false positive rate for X². Similar for FPR-2 and FPR-3; F-1 is F-measure for X¹. Similar for F-2 and F-3

Method	Error (%)	$\widehat{\rho }$	TPR-1	TPR-2	TPR-3	FPR-1	FPR-2	FPR-3	F-1	F-2	F-3
Scenario 1
SIDANet (RS)	1.57	0.87	99.88	99.25	98.00	1.49	4.12	2.28	87.79	67.88	80.24
SIDANet (GS)	1.81	0.87	99.25	98.88	94.25	1.92	1.31	0.92	85.26	88.94	89.93
FNSLDA (Ens)	1.59	0.88	100.00	100.00	100.00	7.25	2.00	2.83	75.80	85.29	82.01
FNSLDA (Stack)	1.50	0.87	100.00	100.00	100.00	8.95	9.04	8.81	79.34	78.67	79.15
Scenario 2
SIDANet (RS)	3.69	0.88	99.50	100.00	91.75	1.40	2.31	1.01	78.85	65.16	74.21
SIDANet (GS)	3.78	0.88	100.00	99.75	86.50	1.39	0.95	0.31	79.18	86.05	85.05
FNSLDA (Ens)	4.03	0.87	100.00	100.00	100.00	7.01	4.46	12.55	52.43	52.91	44.25
FNSLDA (Stack)	3.73	0.85	100.00	100.00	100.00	16.63	16.46	16.80	38.52	38.52	38.49