{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002324747,0.001418996,0.001336169,0.0007801507,0.0006124277,0.0009882838,0.001409978,0.0014408,0.001697971],"category_scores_gemma":[0.005158566,0.0007515932,0.001420907,0.0009692375,0.00140353,0.001629189,0.001246892,0.001934778,0.0004244575],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001126574,"about_ca_system_score_gemma":0.002097277,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009406412,"about_ca_topic_score_gemma":0.0103142,"domain_scores_codex":[0.9987963,0.0005393296,0.00004692563,0.00024565,0.0002681194,0.0001036555],"domain_scores_gemma":[0.9983537,0.001037975,0.0001776246,0.0001150185,0.0002454054,0.00007037167],"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":[0.00007136824,0.00003750702,0.0006360366,0.0001633341,0.00007507655,0.0001058354,0.00009465058,0.8539772,0.003203343,0.07772675,0.002473325,0.06143558],"study_design_scores_gemma":[0.000004309222,0.000007369802,0.00006955567,0.000005102017,0.000003092694,0.00001107282,0.000004787128,0.9859833,0.000200927,0.01333147,0.0003728516,0.000006203446],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003241289,0.0001269822,0.9960324,0.00009515823,0.00001373246,0.00001799035,0.00004621571,0.00006494633,0.000361317],"genre_scores_gemma":[0.2795711,0.0009797853,0.7145266,0.000250878,0.0001533684,0.0003062048,0.0008204686,0.0001769165,0.003214692],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009406412,"threshold_uncertainty_score":0.01870334,"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."}}