{"id":"W4389560506","doi":"10.1016/j.engappai.2023.107668","title":"Incremental semi-supervised graph learning NMF with block-diagonal","year":2023,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Non-negative matrix factorization; Computer science; Matrix decomposition; Dimensionality reduction; Pattern recognition (psychology); Diagonal; Graph; Artificial intelligence; Curse of dimensionality; Coefficient matrix; Block matrix; Regularization (linguistics); Mathematics; Theoretical computer science","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":[0.0001855827,0.0001115001,0.0001087716,0.0002517136,0.0001190532,0.00005171341,0.0004352568,0.00004362941,0.00001800099],"category_scores_gemma":[0.00002802914,0.0001086291,0.00004303632,0.001340286,0.00003704633,0.0001845221,0.0001051279,0.0001542839,0.0002213742],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001417931,"about_ca_system_score_gemma":0.00002540732,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002261394,"about_ca_topic_score_gemma":0.000002297863,"domain_scores_codex":[0.9990601,0.00001298982,0.0002495317,0.0002650254,0.0002061685,0.0002062016],"domain_scores_gemma":[0.9993802,0.0001142827,0.00005872411,0.0002864554,0.00009157932,0.00006875463],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000150187,0.0001445301,0.0002595955,0.00007595382,0.00004486529,0.000004272154,0.001072348,0.6178606,0.1797352,0.04412907,0.0003719753,0.1562867],"study_design_scores_gemma":[0.00003109613,0.00007985208,0.0002710218,0.00006879086,0.000007257908,0.000006288286,0.0003252213,0.6760322,0.3192437,0.002171327,0.001528042,0.0002351747],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1070098,0.00003006252,0.8919712,0.0001794491,0.00005274671,0.0001949249,0.000003699524,0.0004333314,0.0001248059],"genre_scores_gemma":[0.9721648,0.00003119791,0.02749162,0.0000145594,0.00004580077,0.0001901646,0.0000156411,0.00001216798,0.00003404437],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.865155,"threshold_uncertainty_score":0.4429764,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01909905820611718,"score_gpt":0.2456771259542663,"score_spread":0.2265780677481491,"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."}}