{"id":"W2104752854","doi":"","title":"Metric Learning by Collapsing Classes","year":2005,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":665,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Mahalanobis distance; Metric (unit); Metric space; Mathematics; Intrinsic metric; Artificial intelligence; Feature vector; Computer science; Convex metric space; Mathematical optimization; Pattern recognition (psychology); Algorithm; Discrete mathematics","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":[0.00337655,0.001541865,0.002219619,0.003212958,0.001681054,0.002539788,0.004082011,0.001926154,0.004280413],"category_scores_gemma":[0.01414832,0.0009780243,0.001904347,0.0035057,0.002507762,0.005069968,0.008377518,0.003813729,0.00270983],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001804102,"about_ca_system_score_gemma":0.001331846,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003000349,"about_ca_topic_score_gemma":0.002823881,"domain_scores_codex":[0.9946845,0.001134537,0.0003891423,0.001603173,0.001896045,0.0002925894],"domain_scores_gemma":[0.995541,0.001269499,0.0003795924,0.001574852,0.0009670167,0.0002680268],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001556015,0.0001404306,0.00158111,0.0001621775,0.0001152846,0.0001009726,0.0003768345,0.09729218,0.006096112,0.1063275,0.008998886,0.7786528],"study_design_scores_gemma":[0.0000326818,0.0001621057,0.0005228705,0.00004110013,0.00003109954,0.0001933234,0.000100829,0.7272092,0.005917555,0.25043,0.01530305,0.00005618225],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004003265,0.0001211973,0.9942355,0.00009979419,0.00003639105,0.00006776992,0.00006047694,0.0004768662,0.0008988096],"genre_scores_gemma":[0.1076477,0.0002794735,0.8863634,0.0002572466,0.0001291071,0.0004133782,0.0008502666,0.0004427155,0.003616601],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004280413,"threshold_uncertainty_score":0.01785707,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01189598646681285,"score_gpt":0.2456906142936362,"score_spread":0.2337946278268233,"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."}}