TABLE 1.

Scenario 1: RS; randomly select tuning parameters space to search. GS; search entire tuning parameters space. MGSDA (Ens) applies sparse LDA method on separate views and perform classification on the pooled discriminant vectors. MGSDA (Stack) applies sparse LDA on stacked views. TPR-1; true positive rate for X¹. Similar for TPR-2. FPR; false positive rate for X². Similar for FPR-2; F-1 is F-measure for X¹. Similar for F-2. ρ₁ and ρ₂ control the strength of association between X¹ and X². c controls the between-class variability within each view

Method	Error (%)	$\widehat{\rho }$	TPR-1	TPR-2	FPR-1	FPR-2	F-1	F-2
Setting 1
(ρ1 = 0.9, ρ2 = 0.7, c = 0.5)
SIDA (RS)	0.04	0.99	100.00	100.00	0.00	0.00	100.00	100.00
SIDA (GS)	0.05	0.99	100.00	100.00	0.00	0.00	100.00	100.00
sCCA	0.05	0.99	100.00	100.00	1.04	1.32	69.89	69.19
JACA	0.11	1.00	100.00	100.00	3.42	3.86	42.37	38.07
MGSDA (Stack)	0.19	0.84	7.50	8.50	0.00	0.00	16.82	16.20
MGSDA (Ens)	0.33	0.95	14.25	13.50	0.00	0.05	24.65	22.46
Setting 2
(ρ1 = 0.4, ρ2 = 0.2, c = 0.2)
SIDA (RS)	11.32	0.58	100.00	100.00	1.17	1.90	86.56	80.51
SIDA (GS)	11.42	0.58	100.00	99.75	2.28	1.57	68.82	81.85
sCCA	16.20	0.65	100.00	100.00	2.44	1.14	66.70	70.81
JACA	11.32	0.58	100.00	100.00	2.23	1.94	75.92	76.38
MGSDA (Stack)	12.52	0.55	34.25	32.50	0.04	0.06	48.22	46.29
MGSDA (Ens)	17.05	0.61	39.00	37.00	0.04	0.07	53.34	50.09
Setting 3
(ρ1 = 0.15, ρ2 = 0.05, c = 0.12)
SIDA (RS)	31.03	0.14	98.50	97.00	5.07	2.93	41.43	58.05
SIDA (GS)	29.61	0.26	99.00	99.75	2.48	2.85	53.88	56.07
sCCA	34.80	0.20	92.75	93.75	1.10	1.47	74.66	77.45
JACA	29.84	0.19	97.25	97.00	0.74	0.85	81.51	82.53
MGSDA (Stack)	31.55	0.15	28.00	27.00	0.07	0.05	41.53	40.25
MGSDA (Ens)	35.31	0.16	30.75	28.50	0.17	0.01	41.92	43.09