{"id":"W2803390963","doi":"10.1002/wics.1434","title":"A review of quadratic discriminant analysis for high‐dimensional data","year":2018,"lang":"en","type":"review","venue":"Wiley Interdisciplinary Reviews Computational Statistics","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":54,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Quadratic classifier; Exploratory data analysis; Linear discriminant analysis; Curse of dimensionality; Clustering high-dimensional data; Cluster analysis; Mathematics; Covariance; Artificial intelligence; Bayesian probability; Graphical model; Quadratic equation; Machine learning; Computer science; Pattern recognition (psychology); Data mining; Statistics; Support vector machine","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002916887,0.001352421,0.00167403,0.00428921,0.000599411,0.002091795,0.001626732,0.001428873,0.005643396],"category_scores_gemma":[0.006200534,0.0006072628,0.001197525,0.006497836,0.001136677,0.002650138,0.001115175,0.001863922,0.004482855],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001028868,"about_ca_system_score_gemma":0.001727757,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002377416,"about_ca_topic_score_gemma":0.001853981,"domain_scores_codex":[0.9984161,0.000360965,0.000187191,0.0002916412,0.0006794144,0.00006478613],"domain_scores_gemma":[0.9966846,0.002012803,0.0001765034,0.0001436063,0.0009124868,0.00006989342],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0000417239,0.00005977345,0.0007793828,0.006172335,0.0001270667,0.0001422305,0.0001427216,0.002702157,0.001181253,0.02799222,0.04575837,0.9149008],"study_design_scores_gemma":[0.00001638853,0.0001228716,0.002875175,0.002600047,0.0001531666,0.001430327,0.0001637798,0.01148239,0.001611873,0.04760956,0.9317923,0.00014217],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0008685178,0.8937973,0.09288817,0.002294399,0.001421281,0.000053714,0.0002943825,0.0002452401,0.008137021],"genre_scores_gemma":[0.01441373,0.9117988,0.06229526,0.001408898,0.003416772,0.0001469744,0.0007587232,0.0001596382,0.005601251],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.005643396,"threshold_uncertainty_score":0.01887906,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1476410628016731,"score_gpt":0.4255622137364176,"score_spread":0.2779211509347445,"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."}}