{"id":"W2099229603","doi":"10.1109/tpami.2005.183","title":"Principal surfaces from unsupervised kernel regression","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":71,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Principal component regression; Kernel principal component analysis; Initialization; Kernel (algebra); Artificial intelligence; Kernel method; Principal component analysis; Pattern recognition (psychology); Kernel regression; Computer science; Estimator; Mathematics; Nonparametric regression; Dimensionality reduction; Support vector machine; Statistics","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002297146,0.0002511587,0.0004453403,0.0001812058,0.0001721522,0.00004454041,0.0001639673,0.00008553921,0.0009133443],"category_scores_gemma":[0.00002785442,0.0001875192,0.0002089291,0.0003224667,0.00007348481,0.0001210883,0.000003837933,0.000282535,0.0000253483],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003185681,"about_ca_system_score_gemma":0.000009630639,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006359944,"about_ca_topic_score_gemma":0.001919894,"domain_scores_codex":[0.9984415,0.0001314948,0.0004510764,0.0004776346,0.0002682837,0.0002300273],"domain_scores_gemma":[0.9985052,0.0007881164,0.0001058996,0.000377114,0.00005915995,0.0001644912],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004384457,0.0002638725,0.0002200144,0.00001776088,0.0004283609,0.000006061684,0.0004302683,0.01708855,0.0008511587,0.0004223751,0.000007898951,0.9802198],"study_design_scores_gemma":[0.0003712921,0.0001640243,0.001018489,0.0001216969,0.00212789,0.000004793517,0.0002161339,0.7198177,0.2236407,0.05141992,0.0003885957,0.0007088097],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04521462,0.0001154968,0.9538284,0.000249113,0.00007266871,0.0001159873,0.0002384077,0.00006681958,0.00009848038],"genre_scores_gemma":[0.8861274,0.0003626725,0.1128265,0.0001807636,0.00003257266,0.00001572738,0.000007930749,0.00001980089,0.0004266564],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.979511,"threshold_uncertainty_score":0.9999999,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08144094040329365,"score_gpt":0.3843076653121441,"score_spread":0.3028667249088505,"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."}}