{"id":"W2948950091","doi":"10.48550/arxiv.1906.02590","title":"Linear and Quadratic Discriminant Analysis: Tutorial","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":84,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Quadratic classifier; Linear discriminant analysis; Kernel Fisher discriminant analysis; Mathematics; Optimal discriminant analysis; Mahalanobis distance; Artificial intelligence; Pattern recognition (psychology); Principal component analysis; Bayes' theorem; Naive Bayes classifier; Multiple discriminant analysis; Binary classification; Machine learning; Statistics; Classifier (UML); Bayesian probability; Computer science; Support vector machine","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":[0.001597102,0.002055192,0.001221295,0.002541495,0.0004630677,0.001900818,0.0009602032,0.001676426,0.02152957],"category_scores_gemma":[0.003125596,0.0008428604,0.001176236,0.003587499,0.0009054396,0.002760695,0.001180337,0.003034769,0.01829986],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007529936,"about_ca_system_score_gemma":0.0008202411,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001467755,"about_ca_topic_score_gemma":0.001248607,"domain_scores_codex":[0.9990722,0.0002396265,0.00008470334,0.0002105008,0.0003385404,0.00005440124],"domain_scores_gemma":[0.9988126,0.0006856954,0.00005221978,0.000107806,0.0002912233,0.0000504671],"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":[0.00007625385,0.0001511358,0.0006044745,0.001993849,0.0001325508,0.0002959175,0.0002535492,0.008565055,0.003614214,0.1379417,0.2947142,0.5516571],"study_design_scores_gemma":[0.00001124097,0.00007672729,0.001042381,0.0003520879,0.00003526139,0.0008307168,0.00006486696,0.01289067,0.0008973551,0.1084187,0.8753196,0.00006036839],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00250725,0.3860696,0.5085981,0.00413536,0.008535441,0.0002028439,0.001994867,0.002558788,0.08539771],"genre_scores_gemma":[0.03204977,0.3546503,0.4675081,0.005390574,0.02274138,0.0009155529,0.005867605,0.002024379,0.1088523],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02152957,"threshold_uncertainty_score":0.07202351,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06530002424121407,"score_gpt":0.1932816535463205,"score_spread":0.1279816293051065,"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."}}