{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00172307,0.0007152704,0.004113819,0.0005072938,0.0002755628,0.0001017221,0.002969475,0.0001787896,0.000153984],"category_scores_gemma":[0.0005107548,0.0005205825,0.0009423168,0.001500855,0.0001862057,0.0004442999,0.003481009,0.0002757071,0.0002460802],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001059924,"about_ca_system_score_gemma":0.0005200643,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006612264,"about_ca_topic_score_gemma":0.00001100686,"domain_scores_codex":[0.9937223,0.0006887182,0.003178885,0.001380707,0.0006381334,0.0003912174],"domain_scores_gemma":[0.9926779,0.001744423,0.002627498,0.002055329,0.0007105125,0.0001843323],"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":[0.000004211868,0.0001408705,2.704726e-7,0.1383613,0.0006229581,0.000005420913,0.00003208232,0.00003387885,2.84886e-8,0.002592497,0.2758474,0.5823591],"study_design_scores_gemma":[0.0001476943,0.0002388158,0.000002871867,0.2605545,0.006075969,0.00003984299,0.000003000579,0.0483759,8.090747e-8,0.01178368,0.6721605,0.0006171412],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[2.125222e-8,0.5102967,0.4826746,0.00007464377,0.0003361417,0.0012776,0.005305186,0.00002246912,0.00001260621],"genre_scores_gemma":[2.724526e-7,0.6464141,0.3270895,0.000210983,0.0001240325,0.0002890353,0.02579351,0.00002796743,0.00005064151],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.5817419,"threshold_uncertainty_score":0.9997246,"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."}}