{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001667304,0.001037,0.0017637,0.001756272,0.0004112974,0.0007695056,0.001115106,0.0007385306,0.001901725],"category_scores_gemma":[0.002470493,0.0003353595,0.001402854,0.001549152,0.0004523881,0.0008998193,0.0009309975,0.0008875787,0.0009677999],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004850627,"about_ca_system_score_gemma":0.0006025353,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002668352,"about_ca_topic_score_gemma":0.002157855,"domain_scores_codex":[0.9988463,0.0002318717,0.00006462925,0.0003273991,0.000402841,0.0001269898],"domain_scores_gemma":[0.9991835,0.0002622994,0.00009690717,0.0001305027,0.000284434,0.00004230526],"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":[0.0004221247,0.0001751713,0.003033698,0.0001486135,0.0001926873,0.0001346309,0.0001108012,0.1245465,0.04279795,0.003416154,0.005781034,0.8192407],"study_design_scores_gemma":[0.00001638532,0.0001061637,0.001699036,0.00001413942,0.00003925666,0.0001256078,0.00003764645,0.9818751,0.01085547,0.0032829,0.001925762,0.00002244185],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01071526,0.0004260603,0.987755,0.00006362814,0.00002956402,0.00003266138,0.000102905,0.0005156234,0.000359324],"genre_scores_gemma":[0.4833437,0.0005985312,0.5117194,0.0001702219,0.0002023621,0.0002299441,0.001458316,0.0002215885,0.002055989],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002668352,"threshold_uncertainty_score":0.008817673,"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."}}