From: Sparse and smooth canonical correlation analysis through rank-1 matrix approximation
θ x (rad) | θ y (rad) | θ x (rad) | θ y (rad) | θ x (rad) | θ y (rad) | ||
---|---|---|---|---|---|---|---|
Method | N=50 | N=100 | N=200 | ||||
Scenario 4: | CCA | 0.8125 | 0.9956 | 0.5603 | 0.6678 | 0.3390 | 0.4484 |
LS CCA | 0.5275 | 0.7305 | 0.3553 | 0.4711 | 0.2412 | 0.3449 | |
CCA LB | 0.7603 | 0.9209 | 0.2785 | 0.5163 | 0.0149 | 0.3152 | |
PMD | 0.6111 | 0.8273 | 0.2031 | 0.4616 | 0.0397 | 0.3373 | |
Algorithm 2 | 0.8829 | 0.9938 | 0.5288 | 0.6735 | 0.3295 | 0.4447 | |
Algorithm 3 | 0.3990 | 0.6856 | 0.0173 | 0.3237 | 0.0001 | 0.3035 | |
Scenario 5: | CCA | 1.3798 | 1.3764 | 0.8879 | 0.8744 | 0.4700 | 0.4722 |
LS CCA | 0.8538 | 0.8298 | 0.5231 | 0.5187 | 0.3373 | 0.3378 | |
CCA LB | 1.3681 | 1.3659 | 0.7264 | 0.7347 | 0.0478 | 0.0417 | |
PMD | 1.3972 | 1.3542 | 1.1316 | 1.0342 | 0.4082 | 0.3820 | |
Algorithm 2 | 1.3627 | 1.3655 | 0.7413 | 0.8096 | 0.4407 | 0.4605 | |
Algorithm 3 | 1.1185 | 1.0986 | 0.0275 | 0.0271 | 0.0001 | 0.0001 | |
Scenario 6: | CCA | 1.4853 | 1.4854 | 1.4624 | 1.4633 | 1.4249 | 1.4199 |
LS CCA | 1.4589 | 1.4578 | 1.3797 | 1.3838 | 1.1954 | 1.1951 | |
CCA LB | 1.4862 | 1.4851 | 1.4684 | 1.4740 | 1.4830 | 1.4793 | |
PMD | 1.5244 | 1.5130 | 1.4985 | 1.4954 | 1.4553 | 1.4551 | |
Algorithm 2 | 1.4794 | 1.4791 | 1.4512 | 1.4509 | 1.3869 | 1.3790 | |
Algorithm 3 | 1.4633 | 1.4628 | 0.7775 | 0.7885 | 0.0220 | 0.0221 |