{"id":"W2573597659","doi":"10.1109/mmsp.2016.7813403","title":"Generalized dirichlet mixture matching projection for supervised linear dimensionality reduction of proportional data","year":2016,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Dimensionality reduction; Projection (relational algebra); Dirichlet distribution; Divergence (linguistics); Mathematics; Pattern recognition (psychology); Reduction (mathematics); Computer science; Preprocessor; Algorithm; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004379139,0.00009197633,0.0001250314,0.00006581991,0.0001105615,0.00002080274,0.0003545445,0.00006815547,0.0000679573],"category_scores_gemma":[0.00005241199,0.00005419304,0.00005012935,0.0001414751,0.00002791493,0.000941394,0.0001999474,0.0000383902,0.00001084892],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001892467,"about_ca_system_score_gemma":0.00007987047,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004572005,"about_ca_topic_score_gemma":0.000003406827,"domain_scores_codex":[0.9988454,0.00005978838,0.0002820683,0.0004090542,0.0002664191,0.0001372591],"domain_scores_gemma":[0.9990216,0.00004924143,0.0001242247,0.0005285558,0.0002335432,0.00004275816],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000155246,0.0002545944,0.0001480696,0.00008138241,0.00003813621,6.408359e-7,0.0001468032,0.00003847159,0.9006495,0.01644969,0.03870624,0.04333115],"study_design_scores_gemma":[0.004419899,0.0003294311,0.001664853,0.0003796605,0.00005175146,0.00007538812,0.0001263979,0.1604754,0.762644,0.04714904,0.02196818,0.0007159943],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2172718,0.00002147723,0.7780229,0.003559397,0.0004347819,0.0004158203,0.0000713982,0.0001134955,0.00008898412],"genre_scores_gemma":[0.3366552,0.00004972628,0.6606109,0.0001771804,0.0003328001,0.00008637994,0.0003968935,0.00001390492,0.001676999],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1604369,"threshold_uncertainty_score":0.2209928,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06380959374351942,"score_gpt":0.3115505121241675,"score_spread":0.2477409183806481,"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."}}