{"id":"W4399801303","doi":"10.1109/tnnls.2024.3408208","title":"Bi-Level Spectral Feature Selection","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Fundamental Research Funds for the Central Universities; National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Feature selection; Computer science; Cluster analysis; Artificial intelligence; Pattern recognition (psychology); Classifier (UML); Linear classifier; Data mining; Feature (linguistics); Machine learning","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001399891,0.001237585,0.001523305,0.001976646,0.0006336152,0.000867499,0.001296059,0.0008692096,0.002066396],"category_scores_gemma":[0.003636928,0.0002833539,0.00127756,0.001623679,0.0005823,0.001250757,0.00108899,0.0007471681,0.001436743],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004174771,"about_ca_system_score_gemma":0.001065295,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001780808,"about_ca_topic_score_gemma":0.002601281,"domain_scores_codex":[0.998551,0.0003478325,0.00009009101,0.0003526667,0.0004983427,0.000160121],"domain_scores_gemma":[0.9987538,0.0002877314,0.0001080368,0.0002108864,0.0005915081,0.00004813448],"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":[0.0003816274,0.0003269986,0.004086427,0.0001858214,0.0001535307,0.0001156334,0.0001149813,0.06729437,0.04639313,0.00572512,0.009635441,0.8655869],"study_design_scores_gemma":[0.00004323905,0.0001752047,0.003020716,0.00002073513,0.00005395071,0.0001949361,0.00006758828,0.9595877,0.0241038,0.007653596,0.005035122,0.00004349182],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01942789,0.0002062717,0.9778718,0.00008369868,0.00003045462,0.00009185177,0.000172183,0.00125902,0.0008567815],"genre_scores_gemma":[0.4462651,0.0002582018,0.5457004,0.0002825037,0.00009905254,0.0004922587,0.002300767,0.0003488227,0.004252888],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002066396,"threshold_uncertainty_score":0.007403433,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01781432681971721,"score_gpt":0.2351512350278215,"score_spread":0.2173369082081043,"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."}}