Summary Errors
Per Model Errors
Unexpected Crashes

Algorithms: 6

Summary Tables

Total

Algorithm
--------
Value
Blended Mobius Voting Best Conformal GMDS HKM 1 corr HKM 2 corrs
Average Error 0.127693 0.164354 0.190213 0.503847 0.376407 0.383356
Maximal Error 0.325139 0.565471 0.561247 1.02471 1.07637 1.05846

NRW_cat

Algorithm
--------
Value
Blended Mobius Voting Best Conformal GMDS HKM 1 corr HKM 2 corrs
Average Error 0.161419 0.210905 0.234495 0.518131 0.390226 0.378914
Maximal Error 0.386763 0.661901 0.608937 1.05145 1.06842 1.05787

NRW_horse

Algorithm
--------
Value
Blended Mobius Voting Best Conformal GMDS HKM 1 corr HKM 2 corrs
Average Error 0.111079 0.135077 0.16064 0.5185 0.364668 0.371227
Maximal Error 0.240928 0.552939 0.53451 1.07399 1.1976 1.18873

NRW_dog

Algorithm
--------
Value
Blended Mobius Voting Best Conformal GMDS HKM 1 corr HKM 2 corrs
Average Error 0.135329 0.152048 0.20861 0.535072 0.301738 0.302234
Maximal Error 0.343369 0.529557 0.616198 1.05517 1.06322 1.01296

NRW_wolf

Algorithm
--------
Value
Blended Mobius Voting Best Conformal GMDS HKM 1 corr HKM 2 corrs
Average Error 0.0628037 0.0888262 0.143872 0.460783 0.313758 0.335049
Maximal Error 0.18566 0.44687 0.451267 0.932894 0.943816 0.94301

Fourleg

Algorithm
--------
Value
Blended Mobius Voting Best Conformal GMDS HKM 1 corr HKM 2 corrs
Average Error 0.127552 0.169005 0.180892 0.450688 0.416995 0.432688
Maximal Error 0.348307 0.573285 0.554248 0.953268 1.06374 1.04269

Per Experiment Values

Values in each cell: average geodesic error, maximal geodesic error, images
Blended Mobius Voting Best Conformal GMDS HKM 1 corr HKM 2 corrs
cat0_to_384 Ave=0.0542528
Max=0.115191
Dense

Ave=0.0576179
Max=0.111417
Dense
Sparse
Ave=0.0919025
Max=0.373372
Dense

Ave=0.051744
Max=0.126603
Dense
Sparse
Ave=0.390108
Max=1.14061
Dense
Sparse
Ave=0.358144
Max=1.0196
Dense
Sparse
cat1_to_horse15 Ave=0.114617
Max=0.265694
Dense

Ave=0.244583
Max=0.783394
Dense
Sparse
Ave=0.144712
Max=0.397348
Dense

Ave=0.345458
Max=0.785398
Dense
Sparse
Ave=0.359747
Max=1.39108
Dense
Sparse
Ave=0.31242
Max=1.50962
Dense
Sparse
cat2_to_wolf0 Ave=0.0716051
Max=0.164664
Dense

Ave=0.106074
Max=0.302429
Dense
Sparse
Ave=0.331691
Max=0.718869
Dense

Ave=0.725791
Max=1.16241
Dense
Sparse
Ave=0.250474
Max=0.86724
Dense
Sparse
Ave=0.2576
Max=1.20545
Dense
Sparse
cat3_to_396 Ave=0.093836
Max=0.232404
Dense

Ave=0.136619
Max=0.755209
Dense
Sparse
Ave=0.149266
Max=0.461526
Dense

Ave=0.7483
Max=1.25397
Dense
Sparse
Ave=0.706017
Max=1.09815
Dense
Sparse
Ave=0.702957
Max=1.10662
Dense
Sparse
cat4_to_394 Ave=0.0933693
Max=0.224573
Dense

Ave=0.28166
Max=0.897972
Dense
Sparse
Ave=0.164694
Max=0.538887
Dense

Ave=0.293632
Max=0.875203
Dense
Sparse
Ave=0.287977
Max=1.15839
Dense
Sparse
Ave=0.292817
Max=1.15839
Dense
Sparse
cat5_to_dog8 Ave=0.0878782
Max=0.190642
Dense

Ave=0.0805755
Max=0.153227
Dense
Sparse
Ave=0.135449
Max=0.424474
Dense

Ave=0.534407
Max=1.1375
Dense
Sparse
Ave=0.311535
Max=0.969173
Dense
Sparse
Ave=0.295318
Max=0.969173
Dense
Sparse
cat6_to_387 Ave=0.181897
Max=0.635061
Dense

Ave=0.141318
Max=0.274024
Dense
Sparse
Ave=0.216334
Max=0.620228
Dense

Ave=0.3152
Max=1.24858
Dense
Sparse
Ave=0.425042
Max=1.37672
Dense
Sparse
Ave=0.406911
Max=1.37672
Dense
Sparse
cat7_to_dog5 Ave=0.142807
Max=0.643259
Dense

Ave=0.139982
Max=0.724748
Dense
Sparse
Ave=0.162633
Max=0.609723
Dense

Ave=0.760937
Max=1.33372
Dense
Sparse
Ave=0.312469
Max=1.34421
Dense
Sparse
Ave=0.32
Max=1.34421
Dense
Sparse
cat8_to_dog1 Ave=0.0790449
Max=0.148273
Dense

Ave=0.124601
Max=0.75018
Dense
Sparse
Ave=0.118802
Max=0.413508
Dense

Ave=0.658632
Max=1.17964
Dense
Sparse
Ave=0.306357
Max=0.959836
Dense
Sparse
Ave=0.331594
Max=0.959836
Dense
Sparse
cat9_to_382 Ave=0.148421
Max=0.360171
Dense

Ave=0.225033
Max=0.867942
Dense
Sparse
Ave=0.276334
Max=0.840809
Dense

Ave=0.432334
Max=0.953667
Dense
Sparse
Ave=0.360383
Max=0.998807
Dense
Sparse
Ave=0.350684
Max=1.02312
Dense
Sparse
cat10_to_horse6 Ave=0.0951362
Max=0.211467
Dense

Ave=0.11357
Max=0.235973
Dense
Sparse
Ave=0.140955
Max=0.51597
Dense

Ave=0.637315
Max=1.45462
Dense
Sparse
Ave=0.324795
Max=1.08095
Dense
Sparse
Ave=0.345412
Max=1.14392
Dense
Sparse
horse0_to_393 Ave=0.0910199
Max=0.172253
Dense

Ave=0.0911247
Max=0.173376
Dense
Sparse
Ave=0.135464
Max=0.623621
Dense

Ave=0.237528
Max=1.16566
Dense
Sparse
Ave=0.373475
Max=1.31711
Dense
Sparse
Ave=0.35913
Max=1.31711
Dense
Sparse
horse5_to_wolf2 Ave=0.0643298
Max=0.225175
Dense

Ave=0.115401
Max=0.849706
Dense
Sparse
Ave=0.114931
Max=0.375125
Dense

Ave=0.721964
Max=1.27239
Dense
Sparse
Ave=0.621917
Max=1.08966
Dense
Sparse
Ave=0.691189
Max=1.10748
Dense
Sparse
horse6_to_dog6 Ave=0.046633
Max=0.1854
Dense

Ave=0.0535533
Max=0.21929
Dense
Sparse
Ave=0.120124
Max=0.519368
Dense

Ave=0.849156
Max=1.39371
Dense
Sparse
Ave=0.217821
Max=1.18306
Dense
Sparse
Ave=0.214766
Max=1.18306
Dense
Sparse
horse7_to_398
(Excluded)
Ave=0.0800216
Max=0.29411
Dense

Ave=0.0537081
Max=0.15301
Dense
Sparse
Ave=0.133717
Max=0.467293
Dense

Ave=0.694107
Max=1.25382
Dense
Sparse
Ave=0.242889
Max=1.0123
Dense
Sparse
XXX
horse10_to_393 Ave=0.0910279
Max=0.170832
Dense

Ave=0.303451
Max=1.1777
Dense
Sparse
Ave=0.146055
Max=0.584499
Dense

Ave=0.0784146
Max=0.149784
Dense
Sparse
Ave=0.348469
Max=1.31928
Dense
Sparse
Ave=0.346778
Max=1.31928
Dense
Sparse
horse14_to_386 Ave=0.0735275
Max=0.287858
Dense

Ave=0.161061
Max=0.916452
Dense
Sparse
Ave=0.130276
Max=0.464809
Dense

Ave=0.323588
Max=0.866281
Dense
Sparse
Ave=0.534917
Max=1.10256
Dense
Sparse
Ave=0.697948
Max=1.12019
Dense
Sparse
horse15_to_dog6 Ave=0.0531242
Max=0.189266
Dense

Ave=0.0606826
Max=0.145891
Dense
Sparse
Ave=0.0998366
Max=0.470733
Dense

Ave=0.239894
Max=0.901329
Dense
Sparse
Ave=0.221158
Max=0.780693
Dense
Sparse
Ave=0.284925
Max=0.872759
Dense
Sparse
horse17_to_cat9 Ave=0.605631
Max=0.89015
Dense

Ave=0.18679
Max=0.862469
Dense
Sparse
Ave=0.618178
Max=0.947557
Dense

Ave=0.289387
Max=0.989174
Dense
Sparse
Ave=0.347372
Max=1.3408
Dense
Sparse
Ave=0.247698
Max=1.00286
Dense
Sparse
dog0_to_horse14 Ave=0.0619472
Max=0.128542
Dense

Ave=0.0665603
Max=0.1319
Dense
Sparse
Ave=0.10972
Max=0.497234
Dense

Ave=0.843728
Max=1.34311
Dense
Sparse
Ave=0.264491
Max=1.22286
Dense
Sparse
Ave=0.298728
Max=1.29749
Dense
Sparse
dog1_to_horse15 Ave=0.069908
Max=0.146562
Dense

Ave=0.121267
Max=0.29761
Dense
Sparse
Ave=0.149228
Max=0.640509
Dense

Ave=0.273797
Max=0.798644
Dense
Sparse
Ave=0.276593
Max=1.33528
Dense
Sparse
Ave=0.248329
Max=1.31948
Dense
Sparse
dog2_to_horse7 Ave=0.0658107
Max=0.161107
Dense

Ave=0.107232
Max=0.854815
Dense
Sparse
Ave=0.129778
Max=0.542673
Dense

Ave=0.830963
Max=1.34301
Dense
Sparse
Ave=0.206402
Max=0.715494
Dense
Sparse
Ave=0.15586
Max=0.656101
Dense
Sparse
dog3_to_392 Ave=0.0682132
Max=0.180682
Dense

Ave=0.274095
Max=0.660785
Dense
Sparse
Ave=0.193985
Max=0.546839
Dense

Ave=0.156388
Max=0.572598
Dense
Sparse
Ave=0.291874
Max=0.952667
Dense
Sparse
Ave=0.231165
Max=0.92989
Dense
Sparse
dog5_to_cat9 Ave=0.0668186
Max=0.181674
Dense

Ave=0.174916
Max=0.957217
Dense
Sparse
Ave=0.691767
Max=1.04946
Dense

Ave=0.67947
Max=1.13651
Dense
Sparse
Ave=0.24057
Max=1.02775
Dense
Sparse
Ave=0.231244
Max=1.02775
Dense
Sparse
dog6_to_cat2 Ave=0.414611
Max=0.717944
Dense

Ave=0.69499
Max=1.09307
Dense
Sparse
Ave=0.235862
Max=0.734524
Dense

Ave=0.721229
Max=1.24071
Dense
Sparse
Ave=0.300028
Max=0.842428
Dense
Sparse
Ave=0.299321
Max=0.842428
Dense
Sparse
dog7_to_385 Ave=0.245768
Max=0.835448
Dense

Ave=0.0781961
Max=0.224697
Dense
Sparse
Ave=0.196723
Max=0.765457
Dense

Ave=0.7046
Max=1.38643
Dense
Sparse
Ave=0.262167
Max=1.03588
Dense
Sparse
Ave=0.25613
Max=1.03588
Dense
Sparse
dog8_to_385 Ave=0.702209
Max=1.08333
Dense

Ave=0.185482
Max=0.772405
Dense
Sparse
Ave=0.113173
Max=0.420222
Dense

Ave=0.296207
Max=0.806287
Dense
Sparse
Ave=0.334679
Max=1.04066
Dense
Sparse
Ave=0.294241
Max=0.824915
Dense
Sparse
dog10_to_horse6 Ave=0.0604291
Max=0.175696
Dense

Ave=0.182104
Max=0.894591
Dense
Sparse
Ave=0.100029
Max=0.460486
Dense

Ave=0.820786
Max=1.32752
Dense
Sparse
Ave=0.304387
Max=1.36077
Dense
Sparse
Ave=0.28896
Max=1.26615
Dense
Sparse
wolf0_to_dog0 Ave=0.074795
Max=0.18365
Dense

Ave=0.0769575
Max=0.114263
Dense
Sparse
Ave=0.118869
Max=0.440603
Dense

Ave=0.280391
Max=0.835222
Dense
Sparse
Ave=0.27458
Max=1.17871
Dense
Sparse
Ave=0.164416
Max=0.774782
Dense
Sparse
wolf1_to_384 Ave=0.0326398
Max=0.169716
Dense

Ave=0.0434383
Max=0.228974
Dense
Sparse
Ave=0.0606109
Max=0.275198
Dense

Ave=0.0400227
Max=0.225963
Dense
Sparse
Ave=0.204921
Max=0.787799
Dense
Sparse
Ave=0.285769
Max=0.787799
Dense
Sparse
wolf2_to_394 Ave=0.0738687
Max=0.158597
Dense

Ave=0.131228
Max=0.958349
Dense
Sparse
Ave=0.135833
Max=0.547887
Dense

Ave=0.695097
Max=1.26828
Dense
Sparse
Ave=0.277846
Max=0.93555
Dense
Sparse
Ave=0.316784
Max=0.93555
Dense
Sparse
381_to_horse10 Ave=0.061967
Max=0.162986
Dense

Ave=0.0836972
Max=0.197978
Dense
Sparse
Ave=0.10967
Max=0.443203
Dense

Ave=0.767028
Max=1.2453
Dense
Sparse
Ave=0.703807
Max=1.52679
Dense
Sparse
Ave=0.705038
Max=1.52679
Dense
Sparse
382_to_dog10 Ave=0.0752927
Max=0.263087
Dense

Ave=0.153642
Max=0.877854
Dense
Sparse
Ave=0.127804
Max=0.445045
Dense

Ave=0.2868
Max=0.859448
Dense
Sparse
Ave=0.1674
Max=0.641931
Dense
Sparse
Ave=0.133269
Max=0.332686
Dense
Sparse
383_to_cat0 Ave=0.0553761
Max=0.147798
Dense

Ave=0.0921101
Max=0.268415
Dense
Sparse
Ave=0.107438
Max=0.396382
Dense

Ave=0.332886
Max=0.687516
Dense
Sparse
Ave=0.441697
Max=0.798346
Dense
Sparse
Ave=0.451694
Max=0.870995
Dense
Sparse
384_to_dog5 Ave=0.0704358
Max=0.268325
Dense

Ave=0.0776522
Max=0.16513
Dense
Sparse
Ave=0.308995
Max=0.853909
Dense

Ave=0.817937
Max=1.36898
Dense
Sparse
Ave=0.621778
Max=1.33532
Dense
Sparse
Ave=0.713945
Max=1.33532
Dense
Sparse
385_to_383 Ave=0.0540444
Max=0.181392
Dense

Ave=0.0691625
Max=0.193438
Dense
Sparse
Ave=0.106496
Max=0.270137
Dense

Ave=0.675385
Max=1.2631
Dense
Sparse
Ave=0.187799
Max=0.916051
Dense
Sparse
Ave=0.213041
Max=0.916051
Dense
Sparse
386_to_387 Ave=0.126689
Max=0.367716
Dense

Ave=0.0977953
Max=0.21438
Dense
Sparse
Ave=0.153664
Max=0.466061
Dense

Ave=0.728794
Max=1.34138
Dense
Sparse
Ave=0.747352
Max=1.39796
Dense
Sparse
Ave=0.750175
Max=1.39796
Dense
Sparse
387_to_cat10 Ave=0.127379
Max=0.969455
Dense

Ave=0.240864
Max=0.911109
Dense
Sparse
Ave=0.132936
Max=0.40778
Dense

Ave=0.754792
Max=1.39281
Dense
Sparse
Ave=0.560928
Max=1.02993
Dense
Sparse
Ave=0.71561
Max=1.11974
Dense
Sparse
388_to_cat4 Ave=0.098742
Max=0.232316
Dense

Ave=0.1046
Max=0.278461
Dense
Sparse
Ave=0.126747
Max=0.405228
Dense

Ave=0.117264
Max=0.554859
Dense
Sparse
Ave=0.173243
Max=0.600977
Dense
Sparse
Ave=0.201521
Max=0.600977
Dense
Sparse
389_to_381 Ave=0.0487557
Max=0.128564
Dense

Ave=0.136374
Max=0.70977
Dense
Sparse
Ave=0.0683388
Max=0.243575
Dense

Ave=0.777945
Max=1.23731
Dense
Sparse
Ave=0.461001
Max=0.882486
Dense
Sparse
Ave=0.619546
Max=1.0552
Dense
Sparse
390_to_cat3 Ave=0.0776548
Max=0.27004
Dense

Ave=0.383487
Max=1.16365
Dense
Sparse
Ave=0.464919
Max=0.932374
Dense

Ave=0.777312
Max=1.42855
Dense
Sparse
Ave=0.546681
Max=1.21679
Dense
Sparse
Ave=0.368995
Max=1.16552
Dense
Sparse
391_to_cat9 Ave=0.0678179
Max=0.214645
Dense

Ave=0.197964
Max=0.768548
Dense
Sparse
Ave=0.209863
Max=0.631816
Dense

Ave=0.69274
Max=1.0754
Dense
Sparse
Ave=0.608547
Max=0.926985
Dense
Sparse
Ave=0.609334
Max=0.926985
Dense
Sparse
392_to_386 Ave=0.105223
Max=0.39172
Dense

Ave=0.386294
Max=0.774436
Dense
Sparse
Ave=0.17625
Max=0.526799
Dense

Ave=0.377964
Max=0.876002
Dense
Sparse
Ave=0.629779
Max=1.00208
Dense
Sparse
Ave=0.640767
Max=1.06726
Dense
Sparse
393_to_dog5 Ave=0.122797
Max=0.681331
Dense

Ave=0.170868
Max=0.888653
Dense
Sparse
Ave=0.742952
Max=1.42266
Dense

Ave=0.0881617
Max=0.224171
Dense
Sparse
Ave=0.539139
Max=1.27769
Dense
Sparse
Ave=0.748979
Max=1.27769
Dense
Sparse
394_to_cat2 Ave=0.0890712
Max=0.272668
Dense

Ave=0.374584
Max=1.03963
Dense
Sparse
Ave=0.238935
Max=0.746561
Dense

Ave=0.373158
Max=0.78208
Dense
Sparse
Ave=0.317715
Max=1.21544
Dense
Sparse
Ave=0.265005
Max=0.79512
Dense
Sparse
395_to_390
(Excluded)
Ave=0.166026
Max=0.432997
Dense

Ave=0.28873
Max=1.40363
Dense
Sparse
Ave=0.239382
Max=0.846064
Dense

Ave=0.819941
Max=1.43935
Dense
Sparse
Ave=0.784836
Max=1.65235
Dense
Sparse
XXX
396_to_dog1 Ave=0.0627233
Max=0.1598
Dense

Ave=0.0655469
Max=0.135262
Dense
Sparse
Ave=0.107851
Max=0.450327
Dense

Ave=0.322891
Max=0.859776
Dense
Sparse
Ave=0.279593
Max=0.996734
Dense
Sparse
Ave=0.231247
Max=0.996734
Dense
Sparse
397_to_394 Ave=0.0766917
Max=0.213461
Dense

Ave=0.107276
Max=0.237493
Dense
Sparse
Ave=0.147878
Max=0.505262
Dense

Ave=0.767029
Max=1.34064
Dense
Sparse
Ave=0.26664
Max=1.09005
Dense
Sparse
Ave=0.258243
Max=1.09005
Dense
Sparse
398_to_cat9
(Excluded)
Ave=0.0637235
Max=0.158675
Dense

Ave=0.149751
Max=0.632989
Dense
Sparse
Ave=0.11554
Max=0.353998
Dense

Ave=0.609695
Max=1.04461
Dense
Sparse
Ave=0.328194
Max=1.18766
Dense
Sparse
XXX
399_to_cat2 Ave=0.623823
Max=1.03393
Dense

Ave=0.32707
Max=0.700833
Dense
Sparse
Ave=0.164973
Max=0.621286
Dense

Ave=0.638754
Max=1.28159
Dense
Sparse
Ave=0.623057
Max=1.0522
Dense
Sparse
Ave=0.592915
Max=1.04628
Dense
Sparse
400_to_wolf0 Ave=0.0595837
Max=0.212155
Dense

Ave=0.0598582
Max=0.227498
Dense
Sparse
Ave=0.101299
Max=0.349918
Dense

Ave=0.301432
Max=0.833101
Dense
Sparse
Ave=0.252808
Max=0.803935
Dense
Sparse
Ave=0.294534
Max=0.846999
Dense
Sparse

Excluded Due to Crashes

395_to_390 (1) 398_to_cat9 (1) horse7_to_398 (1)