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Table 2 Simulation results part 1

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 1: CCA 0.5395 0.5033 0.3468 0.3475 0.2273 0.2388
  LS CCA 0.4161 0.3697 0.2649 0.2650 0.1784 0.1872
  CCA LB 0.5172 0.5151 0.3310 0.3341 0.2250 0.2228
  PMD 0.2203 0.2420 0.0908 0.0506 0.0207 0.0175
  Algorithm 2 0.5074 0.5189 0.3123 0.3140 0.2225 0.2202
  Algorithm 3 0.2011 0.2191 0.0491 0.0273 0.0044 0.0057
Scenario 2: CCA 0.5091 0.6682 0.3108 0.4123 0.2089 0.2771
  LS CCA 0.3481 0.5083 0.2285 0.3247 0.1605 0.2182
  CCA LB 0.3000 0.3761 0.0227 0.0228 0.0008 0.0009
  PMD 0.2061 0.3068 0.0230 0.0706 0.0043 0.0443
  Algorithm 2 0.5064 0.6462 0.3062 0.4111 0.2061 0.2792
  Algorithm 3 0.1162 0.1508 0.0012 0.0015 0.0001 0.0001
Scenario 3: CCA 0.8699 1.0281 0.6800 0.8254 0.4823 0.6009
  LS CCA 0.6398 0.8314 0.4608 0.6139 0.3116 0.4348
  CCA LB 0.8681 1.0285 0.6575 0.8122 0.3859 0.4938
  PMD 0.7690 0.9080 0.5382 0.6736 0.2736 0.4811
  Algorithm 2 0.8465 0.9876 0.6654 0.8078 0.4345 0.5839
  Algorithm 3 0.3424 0.4571 0.0393 0.0628 0.0001 0.0016