{"id":"W4293795333","doi":"10.1109/tsipn.2022.3202035","title":"Kernel Regression for Matrix-Variate Gaussian Distributed Signals Over Sample Graphs","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Signal and Information Processing over Networks","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Kernel regression; Hyperparameter; Kernel (algebra); Mathematics; Graph kernel; Polynomial kernel; Kernel method; Kernel embedding of distributions; Covariance matrix; Variable kernel density estimation; Artificial intelligence; Pattern recognition (psychology); Estimation of covariance matrices; Covariance; Regression; Algorithm; Computer science; Statistics; Support vector machine; Combinatorics","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":[0.002139003,0.001373551,0.001186311,0.001131221,0.0003639549,0.00116195,0.001748077,0.001200151,0.001935711],"category_scores_gemma":[0.009443945,0.0005485684,0.001034038,0.001811688,0.001237827,0.002155427,0.001242979,0.002326764,0.001254356],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001043775,"about_ca_system_score_gemma":0.0009575402,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005321549,"about_ca_topic_score_gemma":0.004399319,"domain_scores_codex":[0.9987381,0.0005391567,0.00004760785,0.000342143,0.0002422303,0.00009070365],"domain_scores_gemma":[0.9970884,0.001691082,0.0002884642,0.0004512685,0.0004141328,0.00006675543],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001018586,0.00005484702,0.001087585,0.0001386305,0.0001012792,0.00008970842,0.00008161722,0.7943211,0.004531804,0.04012924,0.002977788,0.1563844],"study_design_scores_gemma":[0.000002883926,0.000008455588,0.0001173838,0.00000399259,0.00000397317,0.00001568318,0.000005165501,0.9911013,0.0005054271,0.007681194,0.0005490606,0.000005620295],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001903692,0.0001468274,0.9973348,0.00005398977,0.00001124655,0.00001119357,0.00002596804,0.0003286993,0.0001835984],"genre_scores_gemma":[0.3550346,0.001512643,0.6363197,0.0002573686,0.0001437342,0.0001989702,0.0008292483,0.0006826335,0.005021035],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005321549,"threshold_uncertainty_score":0.01131225,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01188241520242316,"score_gpt":0.2465462466899076,"score_spread":0.2346638314874844,"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."}}