{"id":"W4389009914","doi":"10.1007/s00180-023-01438-1","title":"Wavelet-based Bayesian approximate kernel method for high-dimensional data analysis","year":2023,"lang":"en","type":"article","venue":"Computational Statistics","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"University of Alberta; Alberta Machine Intelligence Institute; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Kernel embedding of distributions; Reproducing kernel Hilbert space; Wavelet; Kernel method; Radial basis function kernel; Mathematics; Kernel (algebra); Variable kernel density estimation; Kernel principal component analysis; Pattern recognition (psychology); Artificial intelligence; Polynomial kernel; Algorithm; Computer science; Support vector machine; Hilbert space; Mathematical analysis; Discrete mathematics","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":[0.002561781,0.0005997593,0.001591016,0.00113848,0.0004873983,0.001259749,0.001820025,0.001116212,0.002432634],"category_scores_gemma":[0.008767469,0.0006198377,0.001217616,0.002058028,0.000815074,0.001908382,0.001773912,0.002281837,0.001572491],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007645568,"about_ca_system_score_gemma":0.002170879,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005499585,"about_ca_topic_score_gemma":0.004946587,"domain_scores_codex":[0.9984687,0.0005165717,0.0001039003,0.0002333313,0.0005756643,0.0001018225],"domain_scores_gemma":[0.996735,0.001585803,0.0002347955,0.0004563234,0.0008814045,0.0001065899],"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":[0.0003971617,0.0002076,0.001460912,0.0004174311,0.0002771068,0.00009962115,0.0001783749,0.3391225,0.01839555,0.07204419,0.005685311,0.5617142],"study_design_scores_gemma":[0.000005077405,0.00001656054,0.000226337,0.000006857154,0.00001249127,0.00002972165,0.000007424739,0.9912729,0.001084262,0.006505415,0.0008220024,0.00001100188],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001001506,0.0000801647,0.9986999,0.00002463135,0.000007598906,0.00000587152,0.00001916719,0.00009676084,0.00006435564],"genre_scores_gemma":[0.1393089,0.0009934063,0.8547087,0.0001183858,0.00009894635,0.0002182721,0.0006836035,0.0003270829,0.003542716],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005499585,"threshold_uncertainty_score":0.01354814,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0497231692914875,"score_gpt":0.3388928568962744,"score_spread":0.2891696876047869,"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."}}