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Table 3 Simulation results part 2

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