{"id":"W3017261075","doi":"10.1109/tpami.2020.2987013","title":"Multiview Feature Selection for Single-View Classification","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; University of Toronto; Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Feature (linguistics); Feature selection; Weighting; Pattern recognition (psychology); Unavailability; Data set; Matching (statistics); Feature extraction; Set (abstract data type); Word error rate; Data mining; Selection (genetic algorithm); Mathematics; Statistics","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":[],"consensus_categories":[],"category_scores_codex":[0.000114281,0.0001674686,0.0002388928,0.0001882632,0.0002015381,0.0001334661,0.0002269897,0.0000761074,0.00005907744],"category_scores_gemma":[0.00000821202,0.00014331,0.0002211551,0.0009716859,0.0000227434,0.0002762247,0.000002755736,0.0001798827,0.00003280311],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002120822,"about_ca_system_score_gemma":0.00001152936,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006469481,"about_ca_topic_score_gemma":0.0001858429,"domain_scores_codex":[0.9988849,0.00006667713,0.0002400711,0.0005030273,0.000144846,0.0001605098],"domain_scores_gemma":[0.9993751,0.0001039933,0.0001010318,0.0001859335,0.0001057022,0.0001282484],"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":[0.00001370413,0.0001047224,0.00008738786,0.00004507191,0.0001523199,4.146823e-7,0.0002446082,0.002580873,0.008621549,0.0000380704,0.00007115171,0.9880401],"study_design_scores_gemma":[0.0001115419,0.0002232458,0.000192679,0.00003702001,0.0003161409,0.000004502532,0.00003599109,0.7502086,0.2469173,0.00009430519,0.00164081,0.0002178672],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0005265565,0.0001520826,0.9942904,0.004508137,0.0001016983,0.0002335517,0.00003165794,0.0001145514,0.00004141112],"genre_scores_gemma":[0.9916893,0.0004297592,0.006046998,0.001641853,0.00002875695,0.00006759435,0.00001522881,0.000008828165,0.0000716706],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9911628,"threshold_uncertainty_score":0.5844011,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05509779539595317,"score_gpt":0.2891769794478262,"score_spread":0.2340791840518731,"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."}}