{"id":"W3086013422","doi":"10.48550/arxiv.2009.08136","title":"Multidimensional Scaling, Sammon Mapping, and Isomap: Tutorial and Survey","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Isomap; Multidimensional scaling; Nonlinear dimensionality reduction; Metric (unit); Kernel (algebra); Pattern recognition (psychology); Artificial intelligence; Embedding; Landmark; Mathematics; Computer science; Dimensionality reduction; Machine learning; Combinatorics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001402263,0.001943529,0.001528251,0.004532286,0.0006201902,0.002007256,0.001081516,0.001489851,0.00549612],"category_scores_gemma":[0.003222457,0.00080391,0.001072503,0.008774732,0.001407991,0.004772918,0.001580419,0.002222139,0.003646462],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007835593,"about_ca_system_score_gemma":0.001039855,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001309046,"about_ca_topic_score_gemma":0.00102948,"domain_scores_codex":[0.999157,0.0002115481,0.00008410028,0.0001923007,0.0003141218,0.00004102737],"domain_scores_gemma":[0.9989493,0.0005546416,0.00006626552,0.0001174666,0.000263298,0.00004903357],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003406038,0.00008151778,0.0007794399,0.003118012,0.00009563389,0.000152012,0.0004055013,0.008593571,0.001957944,0.1503579,0.04961677,0.7848077],"study_design_scores_gemma":[0.00001110234,0.0001398308,0.001792,0.0008523767,0.00007816275,0.001819263,0.0003487642,0.05528602,0.003086071,0.2076752,0.7287558,0.0001553992],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00331747,0.4594313,0.5102954,0.002234483,0.001990264,0.00008924976,0.0004293179,0.0008619187,0.02135064],"genre_scores_gemma":[0.04534714,0.5791278,0.3536654,0.00115642,0.006022699,0.0003442202,0.001387761,0.0005162639,0.01243239],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00549612,"threshold_uncertainty_score":0.01838636,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.112379141278426,"score_gpt":0.1916085623292087,"score_spread":0.0792294210507827,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}