{"id":"W3091528227","doi":"10.1109/lsp.2020.3028006","title":"A Complete Discriminative Tensor Representation Learning for Two-Dimensional Correlation Analysis","year":2020,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Tensor decomposition and applications","field":"Mathematics","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Discriminative model; Representation (politics); Tensor (intrinsic definition); Pattern recognition (psychology); Canonical correlation; Correlation; Linear discriminant analysis","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":[0.0001291111,0.0001524999,0.0002615415,0.0001274074,0.0003831046,0.00009402438,0.0001087738,0.00003637123,0.00005441586],"category_scores_gemma":[0.00008664576,0.0001483686,0.0001815402,0.0006175749,0.00006881476,0.000196016,0.00001454903,0.0001838503,0.00002047507],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004041553,"about_ca_system_score_gemma":0.00001949384,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006660947,"about_ca_topic_score_gemma":0.000001726703,"domain_scores_codex":[0.9987381,0.0000845564,0.000342959,0.0003743619,0.0002730193,0.0001870643],"domain_scores_gemma":[0.9988909,0.0004363679,0.0003028526,0.00008950401,0.0001849721,0.00009534319],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002185934,0.0001364982,0.001235162,0.0001735029,0.0004445821,0.00000633972,0.005168354,0.3929358,0.5812436,0.001782569,0.008436671,0.008218356],"study_design_scores_gemma":[0.0008453026,0.00005419326,0.0009240505,0.00003493809,0.0007855797,0.000003801922,0.0002999199,0.9888692,0.003958422,0.003693559,0.000261911,0.0002691261],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1783176,0.000008179261,0.8114948,0.009507514,0.00002300263,0.0003498843,0.00002418574,0.0001696493,0.0001051198],"genre_scores_gemma":[0.9673291,1.081779e-7,0.02943082,0.002703872,0.0001718104,0.00009441498,0.0001763656,0.00002673687,0.00006677114],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7890115,"threshold_uncertainty_score":0.6050295,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0880582341947177,"score_gpt":0.3496341180044454,"score_spread":0.2615758838097277,"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."}}