{"id":"W4327718153","doi":"10.1016/j.knosys.2023.110465","title":"Robust dual-graph discriminative NMF for data classification","year":2023,"lang":"en","type":"article","venue":"Knowledge-Based Systems","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Northwest University; Shanxi Provincial Key Research and Development Project; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Non-negative matrix factorization; Discriminative model; Outlier; Matrix decomposition; Robustness (evolution); Computer science; Pattern recognition (psychology); Artificial intelligence; Graph; Feature vector; Dual graph; k-nearest neighbors algorithm; Algorithm; Theoretical computer science","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":[0.001280315,0.001310288,0.002061682,0.001395507,0.0008145494,0.001010062,0.002343812,0.002331771,0.003035018],"category_scores_gemma":[0.004271298,0.0005501219,0.001562024,0.001768324,0.0008568199,0.0013448,0.001638338,0.002447883,0.002541358],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000794708,"about_ca_system_score_gemma":0.002004471,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008465497,"about_ca_topic_score_gemma":0.0112707,"domain_scores_codex":[0.9984956,0.0003830277,0.00007257581,0.000437125,0.0003999558,0.0002116845],"domain_scores_gemma":[0.9984766,0.0005220232,0.0001140446,0.0003871761,0.0004210443,0.00007916999],"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":[0.0004005437,0.0003247119,0.0008803507,0.0001965983,0.0001456118,0.0001021101,0.00005415933,0.1216631,0.02574823,0.008282628,0.01443728,0.8277647],"study_design_scores_gemma":[0.00001102434,0.00003057965,0.0003873273,0.000009313982,0.00001450413,0.00005491402,0.00001071174,0.989776,0.003628813,0.00465665,0.001409927,0.00001025336],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007171871,0.0004801778,0.9896423,0.0002331165,0.0001193691,0.0000364327,0.0001909165,0.001425877,0.0007000241],"genre_scores_gemma":[0.348109,0.0006426458,0.6369978,0.0006768761,0.0003029686,0.0003100016,0.003235146,0.0006288434,0.009096744],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008465497,"threshold_uncertainty_score":0.01683247,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2440555756760268,"score_gpt":0.3397255605977658,"score_spread":0.09566998492173903,"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."}}