{"id":"W4292262008","doi":"10.1109/tip.2022.3194701","title":"Variational Bayesian Orthogonal Nonnegative Matrix Factorization Over the Stiefel Manifold","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Non-negative matrix factorization; Stiefel manifold; Orthogonality; Algorithm; Mathematics; Computer science; Matrix decomposition; Cluster analysis; Pattern recognition (psychology); Artificial intelligence; Mathematical optimization; Eigenvalues and eigenvectors","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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.000233679,0.000154538,0.0001018203,0.0001641875,0.001712873,0.000327509,0.0004403479,0.00003986065,0.0005923496],"category_scores_gemma":[0.000006051991,0.0001282183,0.00008044159,0.00065292,0.00003158336,0.001160673,0.00001028404,0.00040906,0.00003242073],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001095005,"about_ca_system_score_gemma":0.0001763575,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001531719,"about_ca_topic_score_gemma":0.000004972682,"domain_scores_codex":[0.9984263,0.0001409199,0.0002357461,0.0003642763,0.0006103495,0.0002223859],"domain_scores_gemma":[0.999347,0.0001170189,0.0001416106,0.0002195484,0.0001177924,0.00005698898],"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":[0.0004370496,0.002820025,0.0002385233,0.0002784246,0.000271466,0.00009193757,0.02372332,0.2414973,0.09849033,0.01275667,0.005270897,0.6141241],"study_design_scores_gemma":[0.0009753256,0.0001641224,0.001180653,0.00006094475,0.00004720431,0.0000645929,0.0007384589,0.9673191,0.01947674,0.007975842,0.001492942,0.0005041088],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002076399,0.00002648669,0.9954188,0.001039754,0.0005755743,0.0002038211,0.00005525857,0.0001693655,0.0004345738],"genre_scores_gemma":[0.9768103,0.000004829322,0.02196275,0.0005238269,0.00006212287,0.0001575611,0.00001284975,0.00001676969,0.0004489298],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9747339,"threshold_uncertainty_score":0.9995868,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01188033608797109,"score_gpt":0.254587850955187,"score_spread":0.242707514867216,"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."}}