{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001509996,0.001211883,0.001120524,0.001270926,0.0006962144,0.00103993,0.001337779,0.0009466832,0.001980917],"category_scores_gemma":[0.004304474,0.000464456,0.001147612,0.002046113,0.001181849,0.002008245,0.001998083,0.002044366,0.001326497],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006400155,"about_ca_system_score_gemma":0.001608882,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002898471,"about_ca_topic_score_gemma":0.002645079,"domain_scores_codex":[0.9987783,0.0004528588,0.00005576304,0.000311498,0.0003044285,0.00009716089],"domain_scores_gemma":[0.9985587,0.0003201927,0.0001731671,0.0003847149,0.0004162844,0.0001469261],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002186104,0.000243669,0.00182522,0.0003812874,0.0001853635,0.0002526063,0.000243643,0.1402438,0.05277704,0.0589747,0.02017204,0.724482],"study_design_scores_gemma":[0.000009292312,0.00008211694,0.000255249,0.00001301993,0.00001925046,0.0001217614,0.0000173831,0.9822566,0.003822899,0.0106045,0.002765137,0.00003280622],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003850072,0.0002901129,0.9949198,0.0001512379,0.00003778184,0.0000215771,0.00006582266,0.0003144176,0.0003491376],"genre_scores_gemma":[0.1940432,0.001098481,0.7993078,0.0004009701,0.0002543724,0.0002387694,0.001206733,0.0002497075,0.00320007],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002898471,"threshold_uncertainty_score":0.007985711,"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."}}