{"id":"W4409255056","doi":"10.1016/j.engappai.2025.110715","title":"Orthogonal Diversity Nonnegative Matrix Factorization for multi-view clustering","year":2025,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Alberta Innovates; China Scholarship Council; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Non-negative matrix factorization; Diversity (politics); Cluster analysis; Matrix (chemical analysis); Nonnegative matrix; Matrix decomposition; Artificial intelligence; Biclustering; Pattern recognition (psychology); Symmetric matrix; Fuzzy clustering; Eigenvalues and eigenvectors; CURE data clustering algorithm","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":[0.002098781,0.001451034,0.001991807,0.001385231,0.001110167,0.001551103,0.002203582,0.001652058,0.0024291],"category_scores_gemma":[0.006593203,0.0007014882,0.001983494,0.002345895,0.0009740735,0.001992526,0.00220386,0.002225174,0.001721209],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007287341,"about_ca_system_score_gemma":0.00137364,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005030414,"about_ca_topic_score_gemma":0.00635449,"domain_scores_codex":[0.99775,0.0008252257,0.0001142979,0.0005080682,0.000590917,0.0002115409],"domain_scores_gemma":[0.9970334,0.001166021,0.0002383788,0.0005496358,0.0008565411,0.0001559993],"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":[0.000498532,0.0003475858,0.001438261,0.0005679268,0.0003848743,0.0002359145,0.0003933229,0.2440191,0.02920585,0.05170323,0.02543108,0.6457744],"study_design_scores_gemma":[0.00001446226,0.00005220994,0.0003541716,0.0000191022,0.00002416214,0.00008065738,0.00005796064,0.9731478,0.001834685,0.02223934,0.002142697,0.00003273327],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002961846,0.0004131123,0.9956886,0.00009631419,0.00005227121,0.00003325033,0.0001307932,0.0002057866,0.0004179494],"genre_scores_gemma":[0.1948158,0.0009263085,0.7980107,0.0002501015,0.000256247,0.0003507839,0.002085108,0.0002212943,0.003083719],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005030414,"threshold_uncertainty_score":0.01109958,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04363394103741704,"score_gpt":0.3179958623138534,"score_spread":0.2743619212764363,"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."}}