{"id":"W4309346662","doi":"10.1007/s11634-022-00526-2","title":"A dual subspace parsimonious mixture of matrix normal distributions","year":2022,"lang":"en","type":"article","venue":"Advances in Data Analysis and Classification","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Identifiability; Subspace topology; Dual (grammatical number); Set (abstract data type); Covariance matrix; Cluster analysis; Algorithm; Principal component analysis; Computer science; Matrix (chemical analysis); Covariance; Mathematics; Column (typography); Pattern recognition (psychology); Mathematical optimization; Data mining; Applied mathematics; Artificial intelligence; Statistics","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":[],"consensus_categories":[],"category_scores_codex":[0.0006463057,0.00008212207,0.0002050921,0.000196726,0.0001543742,0.00003736106,0.0007462246,0.00002672919,0.00001537437],"category_scores_gemma":[0.0000388962,0.0000779227,0.0000466833,0.001836227,0.00005077658,0.0007702144,0.0005376408,0.0001371012,4.793905e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002834001,"about_ca_system_score_gemma":0.00003222933,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002834092,"about_ca_topic_score_gemma":0.0002025551,"domain_scores_codex":[0.9987401,0.0001923004,0.0002675198,0.000444264,0.000218673,0.0001370877],"domain_scores_gemma":[0.9985855,0.00008779094,0.0001923487,0.001059174,0.00003564835,0.00003949555],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002993576,0.0003683829,0.03485721,0.00003714448,0.0002169639,0.00001146263,0.0006199892,0.001282648,0.002421475,0.637034,0.0008338725,0.3222869],"study_design_scores_gemma":[0.0004537415,0.00006725048,0.06779222,0.000008147104,0.0004263414,0.00001523,0.0003366126,0.8499606,0.0003703118,0.02748464,0.05272523,0.0003596973],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00386551,0.002981801,0.9915729,0.0009016966,0.0000532479,0.00006938912,0.0003526467,0.00001611036,0.0001867362],"genre_scores_gemma":[0.8385336,0.0009306381,0.1597092,0.00002392163,0.00001312747,0.00002285894,0.000695982,0.000002705848,0.00006798015],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8486779,"threshold_uncertainty_score":0.3177595,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02317261970497663,"score_gpt":0.3273198306380522,"score_spread":0.3041472109330756,"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."}}