{"id":"W4385602719","doi":"10.3390/electronics12153355","title":"Supervised Dimensionality Reduction of Proportional Data Using Exponential Family Distributions","year":2023,"lang":"en","type":"article","venue":"Electronics","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Dimensionality reduction; Exponential family; Curse of dimensionality; Modal; Algorithm; Heuristic; Projection (relational algebra); Computer science; Reduction (mathematics); Exponential function; Measure (data warehouse); Mathematical optimization; Dimension (graph theory); Mathematics; Pattern recognition (psychology); Artificial intelligence; Data mining; Machine learning","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":[0.002056136,0.0008036746,0.001267819,0.001170425,0.0005892108,0.0009053741,0.001153397,0.0007899507,0.0008523604],"category_scores_gemma":[0.005216962,0.0004725359,0.001417667,0.0009908099,0.0008331275,0.001785353,0.001332388,0.001480132,0.0004173948],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006031854,"about_ca_system_score_gemma":0.001096783,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002331516,"about_ca_topic_score_gemma":0.002477654,"domain_scores_codex":[0.9984554,0.0004996434,0.00008716574,0.0003850899,0.0004665591,0.0001061304],"domain_scores_gemma":[0.9975798,0.001170796,0.0002247774,0.0003895608,0.0005686881,0.00006649001],"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":[0.0002807454,0.0003038311,0.003594506,0.0001845102,0.00019668,0.0001778987,0.0003802429,0.4558341,0.01685714,0.01507973,0.00395047,0.5031602],"study_design_scores_gemma":[0.000004292931,0.00002299539,0.0004861618,0.000004333555,0.000004810726,0.00004253733,0.0000266505,0.9931617,0.001920109,0.003863034,0.0004528276,0.00001062026],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0213586,0.0001104135,0.9778374,0.00006493853,0.00001499659,0.00003031368,0.00004718511,0.000253605,0.0002824836],"genre_scores_gemma":[0.4443396,0.0003264527,0.551594,0.0001162158,0.00006601834,0.0002668684,0.0008141903,0.0001586361,0.002318028],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002331516,"threshold_uncertainty_score":0.01087403,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07840668563455257,"score_gpt":0.3108969895135942,"score_spread":0.2324903038790416,"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."}}