{"id":"W3014913653","doi":"10.17713/ajs.v49i2.907","title":"Fast Approximate Complete-data k-nearest-neighbor Estimation","year":2020,"lang":"en","type":"article","venue":"Austrian Journal of Statistics","topic":"Topological and Geometric Data Analysis","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Agence Nationale de la Recherche","keywords":"Nearest neighbor graph; k-nearest neighbors algorithm; Nearest-neighbor chain algorithm; Best bin first; Nearest neighbor search; Intrinsic dimension; Graph; Sorting; Data set; Set (abstract data type); Dimension (graph theory); Mathematics; Fixed-radius near neighbors; Nearest neighbour algorithm; Large margin nearest neighbor; Manifold (fluid mechanics); Algorithm; Computer science; Combinatorics; Data mining; Statistics; Artificial intelligence; Cluster analysis; Curse of dimensionality","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":[0.00195285,0.001096527,0.002577728,0.001859658,0.0008463467,0.001868814,0.003661634,0.001892387,0.003645729],"category_scores_gemma":[0.01562459,0.0009112939,0.001367179,0.002629821,0.0008230445,0.003278754,0.003173873,0.001990041,0.003495769],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007936051,"about_ca_system_score_gemma":0.001676197,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00599102,"about_ca_topic_score_gemma":0.007090702,"domain_scores_codex":[0.997143,0.0006370042,0.0001683994,0.0006953637,0.001164898,0.0001912788],"domain_scores_gemma":[0.9953873,0.001611538,0.000290071,0.001525671,0.00103633,0.0001490479],"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":[0.0005465184,0.0002029419,0.004168186,0.0006050835,0.0003431548,0.0002696486,0.00025554,0.391548,0.01072699,0.03630413,0.01849977,0.5365301],"study_design_scores_gemma":[0.00002460391,0.00003335694,0.0006364815,0.00001736403,0.00001456919,0.0001519482,0.00003668644,0.9740751,0.002684862,0.01845719,0.003838925,0.0000288538],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005196424,0.0004198229,0.9923009,0.00007515193,0.00005715272,0.00003713895,0.0004356623,0.001071939,0.0004058345],"genre_scores_gemma":[0.1212146,0.0004695063,0.8712096,0.0001231086,0.0001325444,0.0003028605,0.004228135,0.0002672657,0.002052423],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00599102,"threshold_uncertainty_score":0.01219612,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1114835536417602,"score_gpt":0.2919919520773583,"score_spread":0.1805083984355981,"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."}}